The 2026 Guide to AI Agent Builders (And Why They All Need an Action Layer)
The 2026 Guide to AI Agent Builders (And Why They All Need an Action Layer)
TL;DR: The 2026 Guide to AI Agent Builders & The Missing "Body"
From Scripts to Agents: The industry is moving from deterministic Workflow Automation (linear scripts like Zapier) to probabilistic AI Agent Builders (autonomous systems that reason and plan).
The "Brain" vs. "Body" Problem: An agent needs a "Brain" (orchestration logic from frameworks like LangChain, OpenAI AgentKit, or Microsoft Copilot Studio) and a "Body" (the ability to execute actions in external apps).
The Bottleneck: "Brain" frameworks have matured, but building the "Body" remains the biggest engineering hurdle. Developers lose weeks building secure integrations, handling OAuth 2.0, and managing tokens for tools like Salesforce, Jira, and GitHub.
The Solution (Composio): Composio is the leading Agent Action & Integration Layer. It provides the "Body" for any agent:
Managed Authentication: Handles the entire auth lifecycle (OAuth, API keys, refresh tokens) for thousands of end-users, eliminating security risks.
500+ LLM-Ready Tools: A library of maintained connectors optimised for reliable function calling.
Framework Agnostic: Plugs into any "Brain" (LangChain, CrewAI, UiPath, OpenAI, etc.) without vendor lock-in.
Analysts project the AI agents market to surge past $50 billion by 2030.1 That growth reflects how fundamentally businesses are rethinking application development. But behind the projections sits an engineering challenge that doesn't get enough attention.
Building the "brain" of an agent, its decision-making and orchestration logic using frameworks like LangChain or CrewAI, is only the starting point. The real bottleneck? Building the "body," the integration and action layer that connects agents to real-world tools and APIs.
An agent's intelligence doesn't matter if it can't reliably and securely perform tasks in applications like Salesforce, Jira, or Gmail. This guide analyses the top agent "brains" on the market, from developer-first frameworks to enterprise platforms. It also introduces the solution to the "body" problem: the action layer that enables any agent to move from conversation into secure execution.
What Is an AI Agent Builder?
An AI Agent Builder provides the foundation for creating autonomous software that can reason and act. Traditional software follows rigid scripts. An AI agent builder constructs a cognitive architecture with four components:
The Reasoning Engine (The "Brain"): Interfaces for managing Large Language Models (LLMs) like GPT-5 or Claude. This engine acts as a cognitive router, observing objectives and deciding how to act.
Memory (The Context): Mechanisms for short-term persistence (conversation history) and long-term recall (Vector Databases or RAG systems). Memory allows the agent to maintain state across complex, multi-step tasks.
Planning & Orchestration: The logic that allows an agent to break a complex goal (e.g., "Onboard this employee") into a series of sub-tasks, engage in "Chain of Thought" reasoning, and self-correct if a step fails.
Tool Use (The "Body"): The framework connecting the LLM to external environments like APIs, databases, and enterprise software. This allows the agent to execute its plans.
Builders range from Developer-First Frameworks like LangChain and CrewAI (favored by engineers for granular control) to Low-Code Enterprise Platforms like Salesforce Agentforce (favored by operations teams for ease of use).
What to Look for in an AI Agent Builder
When evaluating an agent builder, you're choosing the foundation for your digital workforce. Beyond basic chat capabilities, focus on these architectural requirements:
Cognitive Agnosticism: Can you swap the underlying models? Models from Gemini, OpenAI and Anthropic regularly leapfrog each other. You don't want your agent logic locked into a single provider.
State Management: Can the platform handle long-running processes? Look for "stateful" architectures (like LangGraph) that allow an agent to pause, wait for human input for days, and resume exactly where it left off without losing context.
Grounding & Hallucination Control: Does the builder offer native RAG (Retrieval-Augmented Generation) or "Trust Layers" that force the agent to cite sources from your internal data? Grounding is critical for preventing the agent from inventing facts.
Integration Extensibility: This is where many builders fall short. Does the platform make it easy to give the agent "hands"? Many builders handle reasoning well but struggle with the secure authentication and plumbing required to connect to tools like Jira, GitHub, or Salesforce.
AI Agent Builder vs. Workflow Automation: The Paradigm Shift
The shift from workflow automation to AI agents represents a transition from Deterministic to Probabilistic systems.
Workflow Automation (The "Old" Way)
Tools like legacy Zapier or standard n8n flows operate deterministically. They function on "If-This-Then-That" logic.
Logic: External to the machine (Human-defined).
Flow: Linear (Step A → Step B → Step C).
Failure State: Brittle. If "Step B" changes slightly or data lacks structure, the automation breaks.
Best For: Rigid, repetitive tasks where variables remain known (e.g., "Copy every new row from Excel to a SQL database").
AI Agent Builders (The "New" Way)
Agent Builders create Probabilistic systems. They function on "Goal-Based" logic.
Logic: Internal to the machine (Model-defined).
Flow: Cyclic. The agent observes a goal, formulates a plan, tries a tool, observes the output, and iterates.
Failure State: Adaptive. If "Step B" fails, the agent can reason about the error, retry with new parameters, or formulate an alternative plan.
Best For: Ambiguous tasks requiring judgment (e.g., "Read this angry customer email, research their order history, and draft an appropriate refund offer").
Feature | Workflow Automation | AI Agent Builder |
|---|---|---|
Control Flow | Deterministic: You define every step. | Probabilistic: You define the goal, and the AI defines the steps. |
Input Handling | Requires structured data (JSON, Forms). | Handles unstructured data (Natural Language, Documents). |
Error Handling | Stops on exception. | Retries, replans, or pivots the strategy. |
Complexity | Linear chains. | Cyclic loops and recursive reasoning. |
How to Choose: AI Agent Builder or Automation Tool?
Don't default to AI agents for everything. Agents cost more (token costs) and run slower (latency) than traditional automation scripts. Use this decision matrix:
Criteria | Workflow Automation (n8n, Zapier) | AI Agent Builder (LangChain, CrewAI) |
|---|---|---|
Trigger & Outcome | Always the same | Varies based on context |
Data Type | Structured (spreadsheets, database rows) | Unstructured (emails, voice logs, Slack threads) |
Solution Path | Fixed and predictable | Requires decision-making |
Tool Interaction | Single tool or predictable sequence | Multiple tools in unpredictable order |
Priority | Speed and low cost | Flexibility and judgment |
Example | Syncing contacts from a signup form to a CRM | A "Level 1 Support Agent" that reads a ticket, decides if it's a bug or billing issue, queries the correct documentation, and either solves the problem or routes it to a human |
The "Brains" of the Stack: Top AI Agent Builders & Frameworks
Category | Primary Audience | Key Strength | Key Challenge (The "Body" Problem) |
|---|---|---|---|
Developer-First Frameworks (e.g., LangChain, CrewAI, OpenAI) | Technical teams, developers | Maximum customisation and control over agent logic. Open-source flexibility. | Requires developers to manually build, secure, and maintain every integration, including authentication and tool management. |
Enterprise SaaS Platforms (e.g., Salesforce, ServiceNow, Microsoft Copilot Studio) | Operations, IT, and Sales leaders | Deep context awareness within their specific data ecosystems (CRM or ITSM) and pre-built internal workflows. | Walled Gardens: High friction and cost when agents need to orchestrate actions outside the vendor's ecosystem (e.g., a Salesforce agent updating Jira). |
Cloud-First Platforms (e.g., Google Vertex AI, Amazon Bedrock) | Enterprise teams within a specific cloud ecosystem | Managed infrastructure and straightforward integration with the host cloud's services. | Creates vendor lock-in. Connecting to external, non-native applications reintroduces significant engineering complexity. |
The market for AI agent builders has grown quickly, but the landscape confuses newcomers. We've segmented the leading platforms for building an agent's logic (its "brain") into two distinct categories based on their primary audience and design philosophy.
Developer-First Frameworks (For Maximum Customisation)
For technical teams demanding granular control, these open-source frameworks provide the core building blocks for agent logic. Their flexibility is a significant advantage, but it comes with a trade-off: you must build the entire "body." That means tackling the complex, time-consuming infrastructure for every real-world connection, from authentication to tool maintenance.
1. LangChain
As the de facto open-source standard for developers building LLM-powered applications, LangChain excels at modular orchestration, allowing developers to chain LLMs with other components. Its extensive integrations and vibrant community make it a popular choice for custom agent ecosystems.
While LangChain provides the structure to call a tool, the developer must still build, secure, and maintain the integration. This means handling OAuth flows, managing API keys, and formatting calls for each new tool.
2. CrewAI
CrewAI is an open-source Python framework designed for orchestrating multi-agent systems, enabling collaboration between AI agents assigned specific roles and goals. This role-based architecture works well for tackling complex tasks by breaking them down into specialised sub-tasks.
Like LangChain, CrewAI focuses on agentic logic rather than the underlying plumbing. If an agent needs to access a user's Google Calendar, the developer must implement the entire authentication and integration layer from scratch. That's a significant bottleneck for scalable applications.
3. OpenAI Agent Builder (AgentKit)
OpenAI launched Agent Builder in late 2025 as part of the AgentKit ecosystem, moving from pure code to a visual, node-based architecture. Developers drag and drop agent, tool, and router nodes onto a canvas, making complex workflow creation accessible to broader teams. The platform ships with OpenAI's core strengths: a Code Interpreter for sandboxed Python execution, managed vector stores for file search, and built-in safety guardrails for PII and prompt injection.
The Agent Builder hits significant "body" limitations in production. While the platform uses the Model Context Protocol (MCP) to theoretically connect to thousands of apps, only about 8 integrations (such as Google Drive and Slack) work out of the box. MCP write operations remain unstable, making actions like updating a Salesforce record or processing a Stripe refund risky or impossible without custom engineering.
The platform also locks you entirely into OpenAI's models and lacks event-driven triggers (for example, "run this agent when a database row appears"). As of 2026, Agent Builder excels as a "brain" for rapid prototyping but lacks the robust, framework-agnostic integration layer that mission-critical enterprise systems require.
Enterprise SaaS Platforms (For Business Process Automation)
For large organisations, building agents where their data already lives is often the path of least resistance. Leading SaaS vendors have pivoted from simple "copilots" to autonomous agent builders. These platforms excel at executing workflows within their own walled gardens but face significant challenges when orchestration requires stepping outside their specific ecosystems.
4. Salesforce Agentforce
Salesforce positions the AI agent as the ultimate CRM super-user. Its Agentforce platform centres on the Atlas Reasoning Engine, a "System 2" cognitive core that mimics deliberate human reasoning (planning, refining, acting) rather than just predicting text. Agentforce creates deeply "context-aware" agents by grounding them in the Data 360 (formerly Data Cloud). By harmonising data from sales, service, and marketing, an agent can reason that a customer with a high "Churn Risk" score requires a different response than a loyal advocate.
The architecture relies heavily on Data Cloud. To achieve autonomy, you must first ingest and unify your data into Salesforce's proprietary graph. While Agentforce handles internal Salesforce actions (Flows, Apex) well, connecting to external systems requires significant configuration via MuleSoft or custom integrations. This often leads to a fragmented "body."
5. ServiceNow AI Agents
ServiceNow approaches the agency through IT and Employee Service Management. Its AI Agent Orchestrator targets "Action-First" workflows, including password resets, server provisioning, and approval routing. The platform excels at Multi-Agent Orchestration. In a complex event like a security breach, a central orchestrator can delegate tasks to specialised agents (e.g., a "Security Agent" to isolate a server and a "Communications Agent" to notify stakeholders). The platform uses the Now Platform's CMDB, giving agents inherent knowledge of enterprise infrastructure.
The "Garbage In, Garbage Out" risk is real. If the underlying CMDB data becomes outdated, the agent's actions will fail. Its "Zero-Copy" integration approach via Integration Hub works well for internal actions. Still, it becomes complex and costly (requiring Pro Plus licensing) when building extensive connections to non-native business tools.
6. Microsoft Copilot Studio
Formerly Power Virtual Agents, Copilot Studio positions itself as the "Knowledge Worker's Operating System." Its "Generative Answers" feature enables agents to dynamically ground their responses in internal knowledge sources such as SharePoint, OneDrive, and Dataverse without manual authoring. Teams can build agents that live directly in the "flow of work" within Teams and Microsoft 365.
Copilot Studio is a walled garden optimised for the Microsoft ecosystem. While the platform uses the Power Platform's connector library, accessing data outside Microsoft (e.g., Google Workspace or Jira) often requires "Premium" connectors, which incur additional licensing costs. Strict service limits also throttle high-frequency actions (e.g., message rate limits per environment), making it difficult to use as a high-throughput backend automation engine.
7. UiPath Agent Builder
UiPath approaches agents from an industrial automation lineage. The platform distinguishes itself with DeepRAG, a multi-step research capability that doesn't just summarise search results but actively plans, loops, and verifies citations across complex documents. UiPath agents can also command Robots to perform UI automation, allowing agents to interact with legacy "green screen" mainframes or desktop apps that lack modern APIs.
UiPath targets the Global 2000 as a heavyweight solution. While unmatched for legacy system interaction, its architecture is complex and costly (using "AI Units" and "Robot Units") for simple API-based workflows. Integrating with standard modern SaaS tools often feels like overkill. The platform focuses more on heavy process automation than the agile, conversational interactivity that consumer-facing agents require.
Cloud-First Platforms (For Managed Infrastructure)
For organisations deeply embedded in a major cloud ecosystem, a native AI agent builder offers managed infrastructure and straightforward data integration. This convenience often comes at the price of vendor lock-in and significant complexity when connecting with tools outside their walled gardens.
8. Google Vertex AI
Google's Vertex AI Agent Builder provides a powerful suite of tools for creating and managing AI agents on the Google Cloud Platform (GCP). Its primary strength is its deep, native integration with the entire GCP ecosystem, from BigQuery to Apigee. It supports retrieval-augmented generation (RAG) to ground agents in enterprise data.
This tight integration also creates a weakness. While agents can easily interact with Google services, connecting to external applications such as Salesforce or Jira requires "Integration Connectors" or custom solutions. That reintroduces the very engineering complexity the platform aims to solve.
9. Amazon Bedrock Agents
Amazon Bedrock provides a fully managed service to build agents with a model-agnostic approach, giving developers access to a wide range of foundation models from Amazon, Anthropic, Cohere, and others. Bedrock Agents can orchestrate multi-step tasks and offer enterprise features such as policy controls and observability via Amazon CloudWatch.
Although it is a managed service, deploying a production agent requires significant technical expertise. Developers must configure AWS Lambda functions for tool execution and manage IAM roles. This creates a steep learning curve and deep AWS lock-in. Any action outside the AWS ecosystem requires building the integration and authentication logic from scratch.
10. IBM Watsonx
IBM's WatsonX platform targets the enterprise with a strong emphasis on governance and security. Through Watsonx Orchestrate, the platform enables the creation of AI agents that can automate complex business workflows across numerous enterprise applications. This focus on trust and compliance makes it a viable option for organisations in highly regulated industries.
Its power comes with considerable cost and complexity. Implementing WatsonX often requires specialised skills or professional services. Expanding beyond its catalogue of pre-built integrations means falling back on custom development, making the platform less agile for teams that need to connect to modern SaaS tools quickly.
The Bottleneck: Why an Agent's "Brain" Is Useless Without a "Body"
The frameworks and platforms above provide powerful tools for building an agent's orchestration and decision-making logic. They all share a common limitation: they focus solely on the "brain," leaving the most challenging part of building a production-ready agent to the developer.
The hardest part is the "body," which includes secure authentication with third-party applications, credential management for thousands of users, and reliable execution via well-formed API calls. This is the insecure, time-consuming, and undifferentiated plumbing that separates a clever prototype from a valuable, scalable product.
The Agent-Native Integration & Action Layer — The Missing Infrastructure Every Agent Needs
What Is an Agent Integration & Action Layer?
Developer frameworks provide the scaffolding. Cloud platforms offer infrastructure. A new layer of the agent stack has emerged to solve this critical challenge. These platforms don't function as another "brain." They provide the robust "body" required for production-grade agents, solving the essential problems of integration, authentication, and execution.
Composio: The Agent-Native Integration & Action Platform
Composio takes a different approach than the other builders discussed. It functions as a framework-agnostic, foundational platform designed to solve the single biggest bottleneck in agent development: the integration and action layer.
An agent's "brain" is useless without a "body." Composio provides secure, scalable plumbing to connect any agent (whether built with LangChain, CrewAI, or a proprietary model) to the hundreds of tools it needs to perform meaningful tasks.
Managed Authentication: Solving the Security Challenge
Composio handles complex OAuth 2.0 flows, API keys, and token refreshes for thousands of end-users. Its managed authentication layer securely manages all credentialing, so your application never touches sensitive user credentials. This significantly reduces security risks and engineering overhead.
LLM-Optimised Tools: From Raw APIs to Reliable Actions
Composio provides over 500 pre-built, LLM-optimised toolkits for popular applications like Salesforce, Jira, and Gmail. These tools are structured and maintained, eliminating the tedious manual preparation and validation required for agentic use.
Managed Execution Environments for Complex Tasks
For complex tasks, Composio provides sandboxed execution environments (such as Docker). These environments allow agents to use a combination of remote API tools (e.g., updating Salesforce) and local tools (e.g., editing a file or running a script) to securely perform sophisticated, stateful operations.
Event-Driven, Proactive Agents
Composio Triggers move beyond user-prompted actions. Triggers allow an agent to subscribe to events, such as a new message in Slack or a new issue in GitHub, and to execute a workflow proactively. This enables autonomous automation.
Composio already solves the 'many-tools' context-stuffing problem with its experimental Tool Router, an intelligent routing layer that automatically discovers, authenticates, and executes the right tool for any task from a single endpoint.
This focus on the action layer has significantly increased development velocity. One customer, Assista AI, reduced its go-to-market time for new integrations by 90%. By offloading the complexities of authentication and tool management to Composio, they saved an estimated $20,000 per month in development costs.
Beyond business automation, Composio powers technically complex agents. Developers have used Composio to build Devin-like Software Engineering agents that automatically resolve GitHub issues. Composio makes this possible by providing agents with remote tools (such as the GitHub toolkit) and a suite of local, sandboxed tools (for reading and writing files and executing shell commands) within a secure Docker environment.
Why Other Automation Tools Don’t Work for AI Agents
To understand why a dedicated action layer is necessary, it helps to know why other tool categories, while powerful in their own right, don't suit the dynamic, multi-tenant needs of AI agents.
N8n — Workflow Automation, Not Agentic Integration
N8n is a powerful open-source workflow automation tool with a vast library of pre-built connectors. It excels at creating structured, predictable data pipelines, but its architecture is designed for traditional, linear iPaaS workflows rather than the dynamic, non-deterministic needs of modern AI agents.
N8n's model can't securely manage thousands of individual end-user credentials or handle the unpredictable, conversational interactions that define agentic workflows. That makes it a poor fit for scalable AI products.
Lindy — Fast Prototyping, Limited Depth
Lindy positions itself as a platform for rapidly deploying AI agents to automate simple business tasks without extensive coding. Its simplicity makes it easy to use, but this comes with some downsides. It lacks deep integration and strong authentication. While suitable for rapid prototyping or internal tools, Lindy's capabilities fall short for deep, secure, and multi-tenant integrations necessary for a production-grade AI application serving a large user base.
Glean — Read-Only Search vs Write Actions
Glean leads in AI-powered enterprise search and knowledge discovery, excelling at securely indexing a company's internal knowledge. It's a best-in-class "read-only" system for the knowledge-retrieval part of RAG.
Glean finds answers. It doesn't execute actions. An agent might use Glean to find a relevant customer ticket, but it would still require a separate action layer, such as Composio, to update that ticket in Jira or send a follow-up email through Gmail. That's the crucial difference between data retrieval and task execution.
Platform | Best For | Limitations for AI Agents | Designed for Multi-Tenant User Authentication |
|---|---|---|---|
Composio | Securely connecting AI agents to any app for real-world action. | N/A - Purpose-built for agentic workflows. | Yes |
N8n | Structured, linear data pipelines and internal workflow automation. | Not designed for dynamic, non-deterministic agentic tasks or managing thousands of end-user credentials securely. | No |
Lindy | Rapidly prototyping simple, single-user automations. | Lacks the deep, secure, and scalable integrations needed for production-grade, multi-tenant applications. | No |
Glean | Enterprise search and knowledge retrieval ("read-only" RAG). | Designed to find information, not execute actions. Cannot perform "write" operations like updating a CRM or sending an email. | No |
Comparison Matrix: Choosing Your Complete Agent Stack
Choosing the right tools requires understanding the different layers of the agent stack. "Brain" platforms provide orchestration. The "body" enables real-world action. This matrix highlights the distinct roles of orchestration frameworks versus a dedicated action layer.
Feature | Composio | LangChain | OpenAI Agent Builder | Salesforce Agentforce | ServiceNow AI Agents | Microsoft Copilot Studio | UiPath Agent Builder | CrewAI | Google Vertex AI | Amazon Bedrock |
|---|---|---|---|---|---|---|---|---|---|---|
Primary Use Case | Agent Action & Integration Layer | Visual Agent Builder & LLM Intelligence | CRM/Sales Automation | IT/Employee Workflows | Employee Productivity & M365 Copilots | Legacy System & Process Automation | Multi-Agent Collaboration | |||
Open Source | No | Yes | Partial (Agents SDK only) | No | No | No | No | No (Integrates with open-source tools) | No (Provides access to open-source models) | |
Managed Authentication (for end-users) | Yes | No (Developer builds manually) | No | Partial (Salesforce only) | Partial (Internal ACLs) | Partial (Internal ACLs) | Partial (Internal ACLs) | No (Developer builds manually) | Partial (Via Google ecosystem) | Partial (Via AWS ecosystem) |
No. of Pre-built Tools | 500+ | Varies (Community-driven, manual setup) | 8 native + MCP ecosystem** | Limited (Salesforce / MuleSoft) | Limited (Integration Hub) | 1,000+ (Power Connectors)** | High | Varies (Community-driven, manual setup) | Limited (Primarily Google services) | Limited (Primarily AWS services) |
Handles Token Refresh | Yes (Automated & Managed) | No (Requires custom implementation) | No | Internal only | Internal only | Yes | Yes | No (Requires custom implementation) | Partial (Within GCP) | Partial (Within AWS) |
Event-Driven Triggers | Yes | No (Requires custom code) | No (Conversation-First) | Yes | No (Conversation-First) | Yes | Yes | No (Requires custom code) | Yes (Via Google Cloud Functions) | Yes (Via AWS Lambda) |
Framework Agnostic | Yes | Yes | No | No | No | No | No | Yes | No | No |
Pricing Model | SaaS / Usage | Open Core | Pay-as-you-go | Consumption (Flex Credits) | Consumption (Flex Credits) | Subscription + Consumption | Unified Consumption | Open Core | Pay-as-you-go | Pay-as-you-go |
*Note on OpenAI: MCP ecosystem claims 8,000+ apps, but write operations remain limited or buggy on many servers. The platform is still in alpha/beta as of December 2025.
**Note on Microsoft: While Microsoft offers many connectors, many external (non-Microsoft) connections require "Premium" licensing and don't handle external end-user OAuth as smoothly as native internal flows.
Conclusion: Stop Building Plumbing, Start Building Value
Building a production-ready AI agent requires more than a powerful "brain" like LangChain or Vertex AI. The bottleneck separating prototype from product remains the "body," a secure, scalable action layer.
By abstracting away the complexities of integration, authentication, and execution, developers can shift focus from building brittle plumbing to shipping agents that deliver tangible business value. Reliably connecting logic to the real world matters as much as the logic itself.
Explore our 500+ toolkits or sign up for a free developer account to connect your agent in minutes.
Frequently Asked Questions
Is Composio an Agent Builder platform?
No. Agent builders like LangChain or CrewAI provide the "brain" for an agent's logic. Composio provides the essential "body." We function as an agent-native Action and Integration Platform that handles complex plumbing, including managed authentication and LLM-ready tools. This allows any agent "brain" to connect to real-world applications and perform meaningful tasks securely and reliably.
How does Composio integrate with AI Agent Builders like LangChain, CrewAI, Salesforce Agentforce and ServiceNow Agent Orchestrator?
Composio is framework-agnostic and functions as a modular component for any agent stack. Your agent "brain," whether built with LangChain, CrewAI, or OpenAI, can call upon Composio's 500+ pre-built tools to execute actions. We provide a secure, authenticated connection to apps like Salesforce or Jira, allowing your agent to focus on orchestration, not plumbing.
Can I use an Agent Builder, such as LangChain or CrewAI, without manually building integrations?
Yes. LangChain and CrewAI handle orchestration logic, but they leave authentication, credential management, and API formatting to developers. Composio plugs directly into both frameworks as the action layer, providing 500+ pre-built, LLM-ready tools with managed authentication. Your agent code stays focused on logic while Composio handles the integration plumbing, including OAuth flows, token refreshes, and secure credential storage for your end-users.
How do I connect an AI agent to Salesforce, Jira, or Gmail?
You have two options. You can build the integration yourself, which means implementing OAuth flows, managing API credentials, handling token refreshes, and formatting requests for each application. For a single tool, this can take weeks of engineering work.
Or you can use Composio's pre-built toolkits. Composio provides LLM-ready connectors for Salesforce, Jira, Gmail, and 500+ other applications. The platform handles authentication and credential management for your end users, so your agent can execute actions such as updating a Salesforce record, creating a Jira ticket, or sending an email within minutes of setup.
TL;DR: The 2026 Guide to AI Agent Builders & The Missing "Body"
From Scripts to Agents: The industry is moving from deterministic Workflow Automation (linear scripts like Zapier) to probabilistic AI Agent Builders (autonomous systems that reason and plan).
The "Brain" vs. "Body" Problem: An agent needs a "Brain" (orchestration logic from frameworks like LangChain, OpenAI AgentKit, or Microsoft Copilot Studio) and a "Body" (the ability to execute actions in external apps).
The Bottleneck: "Brain" frameworks have matured, but building the "Body" remains the biggest engineering hurdle. Developers lose weeks building secure integrations, handling OAuth 2.0, and managing tokens for tools like Salesforce, Jira, and GitHub.
The Solution (Composio): Composio is the leading Agent Action & Integration Layer. It provides the "Body" for any agent:
Managed Authentication: Handles the entire auth lifecycle (OAuth, API keys, refresh tokens) for thousands of end-users, eliminating security risks.
500+ LLM-Ready Tools: A library of maintained connectors optimised for reliable function calling.
Framework Agnostic: Plugs into any "Brain" (LangChain, CrewAI, UiPath, OpenAI, etc.) without vendor lock-in.
Analysts project the AI agents market to surge past $50 billion by 2030.1 That growth reflects how fundamentally businesses are rethinking application development. But behind the projections sits an engineering challenge that doesn't get enough attention.
Building the "brain" of an agent, its decision-making and orchestration logic using frameworks like LangChain or CrewAI, is only the starting point. The real bottleneck? Building the "body," the integration and action layer that connects agents to real-world tools and APIs.
An agent's intelligence doesn't matter if it can't reliably and securely perform tasks in applications like Salesforce, Jira, or Gmail. This guide analyses the top agent "brains" on the market, from developer-first frameworks to enterprise platforms. It also introduces the solution to the "body" problem: the action layer that enables any agent to move from conversation into secure execution.
What Is an AI Agent Builder?
An AI Agent Builder provides the foundation for creating autonomous software that can reason and act. Traditional software follows rigid scripts. An AI agent builder constructs a cognitive architecture with four components:
The Reasoning Engine (The "Brain"): Interfaces for managing Large Language Models (LLMs) like GPT-5 or Claude. This engine acts as a cognitive router, observing objectives and deciding how to act.
Memory (The Context): Mechanisms for short-term persistence (conversation history) and long-term recall (Vector Databases or RAG systems). Memory allows the agent to maintain state across complex, multi-step tasks.
Planning & Orchestration: The logic that allows an agent to break a complex goal (e.g., "Onboard this employee") into a series of sub-tasks, engage in "Chain of Thought" reasoning, and self-correct if a step fails.
Tool Use (The "Body"): The framework connecting the LLM to external environments like APIs, databases, and enterprise software. This allows the agent to execute its plans.
Builders range from Developer-First Frameworks like LangChain and CrewAI (favored by engineers for granular control) to Low-Code Enterprise Platforms like Salesforce Agentforce (favored by operations teams for ease of use).
What to Look for in an AI Agent Builder
When evaluating an agent builder, you're choosing the foundation for your digital workforce. Beyond basic chat capabilities, focus on these architectural requirements:
Cognitive Agnosticism: Can you swap the underlying models? Models from Gemini, OpenAI and Anthropic regularly leapfrog each other. You don't want your agent logic locked into a single provider.
State Management: Can the platform handle long-running processes? Look for "stateful" architectures (like LangGraph) that allow an agent to pause, wait for human input for days, and resume exactly where it left off without losing context.
Grounding & Hallucination Control: Does the builder offer native RAG (Retrieval-Augmented Generation) or "Trust Layers" that force the agent to cite sources from your internal data? Grounding is critical for preventing the agent from inventing facts.
Integration Extensibility: This is where many builders fall short. Does the platform make it easy to give the agent "hands"? Many builders handle reasoning well but struggle with the secure authentication and plumbing required to connect to tools like Jira, GitHub, or Salesforce.
AI Agent Builder vs. Workflow Automation: The Paradigm Shift
The shift from workflow automation to AI agents represents a transition from Deterministic to Probabilistic systems.
Workflow Automation (The "Old" Way)
Tools like legacy Zapier or standard n8n flows operate deterministically. They function on "If-This-Then-That" logic.
Logic: External to the machine (Human-defined).
Flow: Linear (Step A → Step B → Step C).
Failure State: Brittle. If "Step B" changes slightly or data lacks structure, the automation breaks.
Best For: Rigid, repetitive tasks where variables remain known (e.g., "Copy every new row from Excel to a SQL database").
AI Agent Builders (The "New" Way)
Agent Builders create Probabilistic systems. They function on "Goal-Based" logic.
Logic: Internal to the machine (Model-defined).
Flow: Cyclic. The agent observes a goal, formulates a plan, tries a tool, observes the output, and iterates.
Failure State: Adaptive. If "Step B" fails, the agent can reason about the error, retry with new parameters, or formulate an alternative plan.
Best For: Ambiguous tasks requiring judgment (e.g., "Read this angry customer email, research their order history, and draft an appropriate refund offer").
Feature | Workflow Automation | AI Agent Builder |
|---|---|---|
Control Flow | Deterministic: You define every step. | Probabilistic: You define the goal, and the AI defines the steps. |
Input Handling | Requires structured data (JSON, Forms). | Handles unstructured data (Natural Language, Documents). |
Error Handling | Stops on exception. | Retries, replans, or pivots the strategy. |
Complexity | Linear chains. | Cyclic loops and recursive reasoning. |
How to Choose: AI Agent Builder or Automation Tool?
Don't default to AI agents for everything. Agents cost more (token costs) and run slower (latency) than traditional automation scripts. Use this decision matrix:
Criteria | Workflow Automation (n8n, Zapier) | AI Agent Builder (LangChain, CrewAI) |
|---|---|---|
Trigger & Outcome | Always the same | Varies based on context |
Data Type | Structured (spreadsheets, database rows) | Unstructured (emails, voice logs, Slack threads) |
Solution Path | Fixed and predictable | Requires decision-making |
Tool Interaction | Single tool or predictable sequence | Multiple tools in unpredictable order |
Priority | Speed and low cost | Flexibility and judgment |
Example | Syncing contacts from a signup form to a CRM | A "Level 1 Support Agent" that reads a ticket, decides if it's a bug or billing issue, queries the correct documentation, and either solves the problem or routes it to a human |
The "Brains" of the Stack: Top AI Agent Builders & Frameworks
Category | Primary Audience | Key Strength | Key Challenge (The "Body" Problem) |
|---|---|---|---|
Developer-First Frameworks (e.g., LangChain, CrewAI, OpenAI) | Technical teams, developers | Maximum customisation and control over agent logic. Open-source flexibility. | Requires developers to manually build, secure, and maintain every integration, including authentication and tool management. |
Enterprise SaaS Platforms (e.g., Salesforce, ServiceNow, Microsoft Copilot Studio) | Operations, IT, and Sales leaders | Deep context awareness within their specific data ecosystems (CRM or ITSM) and pre-built internal workflows. | Walled Gardens: High friction and cost when agents need to orchestrate actions outside the vendor's ecosystem (e.g., a Salesforce agent updating Jira). |
Cloud-First Platforms (e.g., Google Vertex AI, Amazon Bedrock) | Enterprise teams within a specific cloud ecosystem | Managed infrastructure and straightforward integration with the host cloud's services. | Creates vendor lock-in. Connecting to external, non-native applications reintroduces significant engineering complexity. |
The market for AI agent builders has grown quickly, but the landscape confuses newcomers. We've segmented the leading platforms for building an agent's logic (its "brain") into two distinct categories based on their primary audience and design philosophy.
Developer-First Frameworks (For Maximum Customisation)
For technical teams demanding granular control, these open-source frameworks provide the core building blocks for agent logic. Their flexibility is a significant advantage, but it comes with a trade-off: you must build the entire "body." That means tackling the complex, time-consuming infrastructure for every real-world connection, from authentication to tool maintenance.
1. LangChain
As the de facto open-source standard for developers building LLM-powered applications, LangChain excels at modular orchestration, allowing developers to chain LLMs with other components. Its extensive integrations and vibrant community make it a popular choice for custom agent ecosystems.
While LangChain provides the structure to call a tool, the developer must still build, secure, and maintain the integration. This means handling OAuth flows, managing API keys, and formatting calls for each new tool.
2. CrewAI
CrewAI is an open-source Python framework designed for orchestrating multi-agent systems, enabling collaboration between AI agents assigned specific roles and goals. This role-based architecture works well for tackling complex tasks by breaking them down into specialised sub-tasks.
Like LangChain, CrewAI focuses on agentic logic rather than the underlying plumbing. If an agent needs to access a user's Google Calendar, the developer must implement the entire authentication and integration layer from scratch. That's a significant bottleneck for scalable applications.
3. OpenAI Agent Builder (AgentKit)
OpenAI launched Agent Builder in late 2025 as part of the AgentKit ecosystem, moving from pure code to a visual, node-based architecture. Developers drag and drop agent, tool, and router nodes onto a canvas, making complex workflow creation accessible to broader teams. The platform ships with OpenAI's core strengths: a Code Interpreter for sandboxed Python execution, managed vector stores for file search, and built-in safety guardrails for PII and prompt injection.
The Agent Builder hits significant "body" limitations in production. While the platform uses the Model Context Protocol (MCP) to theoretically connect to thousands of apps, only about 8 integrations (such as Google Drive and Slack) work out of the box. MCP write operations remain unstable, making actions like updating a Salesforce record or processing a Stripe refund risky or impossible without custom engineering.
The platform also locks you entirely into OpenAI's models and lacks event-driven triggers (for example, "run this agent when a database row appears"). As of 2026, Agent Builder excels as a "brain" for rapid prototyping but lacks the robust, framework-agnostic integration layer that mission-critical enterprise systems require.
Enterprise SaaS Platforms (For Business Process Automation)
For large organisations, building agents where their data already lives is often the path of least resistance. Leading SaaS vendors have pivoted from simple "copilots" to autonomous agent builders. These platforms excel at executing workflows within their own walled gardens but face significant challenges when orchestration requires stepping outside their specific ecosystems.
4. Salesforce Agentforce
Salesforce positions the AI agent as the ultimate CRM super-user. Its Agentforce platform centres on the Atlas Reasoning Engine, a "System 2" cognitive core that mimics deliberate human reasoning (planning, refining, acting) rather than just predicting text. Agentforce creates deeply "context-aware" agents by grounding them in the Data 360 (formerly Data Cloud). By harmonising data from sales, service, and marketing, an agent can reason that a customer with a high "Churn Risk" score requires a different response than a loyal advocate.
The architecture relies heavily on Data Cloud. To achieve autonomy, you must first ingest and unify your data into Salesforce's proprietary graph. While Agentforce handles internal Salesforce actions (Flows, Apex) well, connecting to external systems requires significant configuration via MuleSoft or custom integrations. This often leads to a fragmented "body."
5. ServiceNow AI Agents
ServiceNow approaches the agency through IT and Employee Service Management. Its AI Agent Orchestrator targets "Action-First" workflows, including password resets, server provisioning, and approval routing. The platform excels at Multi-Agent Orchestration. In a complex event like a security breach, a central orchestrator can delegate tasks to specialised agents (e.g., a "Security Agent" to isolate a server and a "Communications Agent" to notify stakeholders). The platform uses the Now Platform's CMDB, giving agents inherent knowledge of enterprise infrastructure.
The "Garbage In, Garbage Out" risk is real. If the underlying CMDB data becomes outdated, the agent's actions will fail. Its "Zero-Copy" integration approach via Integration Hub works well for internal actions. Still, it becomes complex and costly (requiring Pro Plus licensing) when building extensive connections to non-native business tools.
6. Microsoft Copilot Studio
Formerly Power Virtual Agents, Copilot Studio positions itself as the "Knowledge Worker's Operating System." Its "Generative Answers" feature enables agents to dynamically ground their responses in internal knowledge sources such as SharePoint, OneDrive, and Dataverse without manual authoring. Teams can build agents that live directly in the "flow of work" within Teams and Microsoft 365.
Copilot Studio is a walled garden optimised for the Microsoft ecosystem. While the platform uses the Power Platform's connector library, accessing data outside Microsoft (e.g., Google Workspace or Jira) often requires "Premium" connectors, which incur additional licensing costs. Strict service limits also throttle high-frequency actions (e.g., message rate limits per environment), making it difficult to use as a high-throughput backend automation engine.
7. UiPath Agent Builder
UiPath approaches agents from an industrial automation lineage. The platform distinguishes itself with DeepRAG, a multi-step research capability that doesn't just summarise search results but actively plans, loops, and verifies citations across complex documents. UiPath agents can also command Robots to perform UI automation, allowing agents to interact with legacy "green screen" mainframes or desktop apps that lack modern APIs.
UiPath targets the Global 2000 as a heavyweight solution. While unmatched for legacy system interaction, its architecture is complex and costly (using "AI Units" and "Robot Units") for simple API-based workflows. Integrating with standard modern SaaS tools often feels like overkill. The platform focuses more on heavy process automation than the agile, conversational interactivity that consumer-facing agents require.
Cloud-First Platforms (For Managed Infrastructure)
For organisations deeply embedded in a major cloud ecosystem, a native AI agent builder offers managed infrastructure and straightforward data integration. This convenience often comes at the price of vendor lock-in and significant complexity when connecting with tools outside their walled gardens.
8. Google Vertex AI
Google's Vertex AI Agent Builder provides a powerful suite of tools for creating and managing AI agents on the Google Cloud Platform (GCP). Its primary strength is its deep, native integration with the entire GCP ecosystem, from BigQuery to Apigee. It supports retrieval-augmented generation (RAG) to ground agents in enterprise data.
This tight integration also creates a weakness. While agents can easily interact with Google services, connecting to external applications such as Salesforce or Jira requires "Integration Connectors" or custom solutions. That reintroduces the very engineering complexity the platform aims to solve.
9. Amazon Bedrock Agents
Amazon Bedrock provides a fully managed service to build agents with a model-agnostic approach, giving developers access to a wide range of foundation models from Amazon, Anthropic, Cohere, and others. Bedrock Agents can orchestrate multi-step tasks and offer enterprise features such as policy controls and observability via Amazon CloudWatch.
Although it is a managed service, deploying a production agent requires significant technical expertise. Developers must configure AWS Lambda functions for tool execution and manage IAM roles. This creates a steep learning curve and deep AWS lock-in. Any action outside the AWS ecosystem requires building the integration and authentication logic from scratch.
10. IBM Watsonx
IBM's WatsonX platform targets the enterprise with a strong emphasis on governance and security. Through Watsonx Orchestrate, the platform enables the creation of AI agents that can automate complex business workflows across numerous enterprise applications. This focus on trust and compliance makes it a viable option for organisations in highly regulated industries.
Its power comes with considerable cost and complexity. Implementing WatsonX often requires specialised skills or professional services. Expanding beyond its catalogue of pre-built integrations means falling back on custom development, making the platform less agile for teams that need to connect to modern SaaS tools quickly.
The Bottleneck: Why an Agent's "Brain" Is Useless Without a "Body"
The frameworks and platforms above provide powerful tools for building an agent's orchestration and decision-making logic. They all share a common limitation: they focus solely on the "brain," leaving the most challenging part of building a production-ready agent to the developer.
The hardest part is the "body," which includes secure authentication with third-party applications, credential management for thousands of users, and reliable execution via well-formed API calls. This is the insecure, time-consuming, and undifferentiated plumbing that separates a clever prototype from a valuable, scalable product.
The Agent-Native Integration & Action Layer — The Missing Infrastructure Every Agent Needs
What Is an Agent Integration & Action Layer?
Developer frameworks provide the scaffolding. Cloud platforms offer infrastructure. A new layer of the agent stack has emerged to solve this critical challenge. These platforms don't function as another "brain." They provide the robust "body" required for production-grade agents, solving the essential problems of integration, authentication, and execution.
Composio: The Agent-Native Integration & Action Platform
Composio takes a different approach than the other builders discussed. It functions as a framework-agnostic, foundational platform designed to solve the single biggest bottleneck in agent development: the integration and action layer.
An agent's "brain" is useless without a "body." Composio provides secure, scalable plumbing to connect any agent (whether built with LangChain, CrewAI, or a proprietary model) to the hundreds of tools it needs to perform meaningful tasks.
Managed Authentication: Solving the Security Challenge
Composio handles complex OAuth 2.0 flows, API keys, and token refreshes for thousands of end-users. Its managed authentication layer securely manages all credentialing, so your application never touches sensitive user credentials. This significantly reduces security risks and engineering overhead.
LLM-Optimised Tools: From Raw APIs to Reliable Actions
Composio provides over 500 pre-built, LLM-optimised toolkits for popular applications like Salesforce, Jira, and Gmail. These tools are structured and maintained, eliminating the tedious manual preparation and validation required for agentic use.
Managed Execution Environments for Complex Tasks
For complex tasks, Composio provides sandboxed execution environments (such as Docker). These environments allow agents to use a combination of remote API tools (e.g., updating Salesforce) and local tools (e.g., editing a file or running a script) to securely perform sophisticated, stateful operations.
Event-Driven, Proactive Agents
Composio Triggers move beyond user-prompted actions. Triggers allow an agent to subscribe to events, such as a new message in Slack or a new issue in GitHub, and to execute a workflow proactively. This enables autonomous automation.
Composio already solves the 'many-tools' context-stuffing problem with its experimental Tool Router, an intelligent routing layer that automatically discovers, authenticates, and executes the right tool for any task from a single endpoint.
This focus on the action layer has significantly increased development velocity. One customer, Assista AI, reduced its go-to-market time for new integrations by 90%. By offloading the complexities of authentication and tool management to Composio, they saved an estimated $20,000 per month in development costs.
Beyond business automation, Composio powers technically complex agents. Developers have used Composio to build Devin-like Software Engineering agents that automatically resolve GitHub issues. Composio makes this possible by providing agents with remote tools (such as the GitHub toolkit) and a suite of local, sandboxed tools (for reading and writing files and executing shell commands) within a secure Docker environment.
Why Other Automation Tools Don’t Work for AI Agents
To understand why a dedicated action layer is necessary, it helps to know why other tool categories, while powerful in their own right, don't suit the dynamic, multi-tenant needs of AI agents.
N8n — Workflow Automation, Not Agentic Integration
N8n is a powerful open-source workflow automation tool with a vast library of pre-built connectors. It excels at creating structured, predictable data pipelines, but its architecture is designed for traditional, linear iPaaS workflows rather than the dynamic, non-deterministic needs of modern AI agents.
N8n's model can't securely manage thousands of individual end-user credentials or handle the unpredictable, conversational interactions that define agentic workflows. That makes it a poor fit for scalable AI products.
Lindy — Fast Prototyping, Limited Depth
Lindy positions itself as a platform for rapidly deploying AI agents to automate simple business tasks without extensive coding. Its simplicity makes it easy to use, but this comes with some downsides. It lacks deep integration and strong authentication. While suitable for rapid prototyping or internal tools, Lindy's capabilities fall short for deep, secure, and multi-tenant integrations necessary for a production-grade AI application serving a large user base.
Glean — Read-Only Search vs Write Actions
Glean leads in AI-powered enterprise search and knowledge discovery, excelling at securely indexing a company's internal knowledge. It's a best-in-class "read-only" system for the knowledge-retrieval part of RAG.
Glean finds answers. It doesn't execute actions. An agent might use Glean to find a relevant customer ticket, but it would still require a separate action layer, such as Composio, to update that ticket in Jira or send a follow-up email through Gmail. That's the crucial difference between data retrieval and task execution.
Platform | Best For | Limitations for AI Agents | Designed for Multi-Tenant User Authentication |
|---|---|---|---|
Composio | Securely connecting AI agents to any app for real-world action. | N/A - Purpose-built for agentic workflows. | Yes |
N8n | Structured, linear data pipelines and internal workflow automation. | Not designed for dynamic, non-deterministic agentic tasks or managing thousands of end-user credentials securely. | No |
Lindy | Rapidly prototyping simple, single-user automations. | Lacks the deep, secure, and scalable integrations needed for production-grade, multi-tenant applications. | No |
Glean | Enterprise search and knowledge retrieval ("read-only" RAG). | Designed to find information, not execute actions. Cannot perform "write" operations like updating a CRM or sending an email. | No |
Comparison Matrix: Choosing Your Complete Agent Stack
Choosing the right tools requires understanding the different layers of the agent stack. "Brain" platforms provide orchestration. The "body" enables real-world action. This matrix highlights the distinct roles of orchestration frameworks versus a dedicated action layer.
Feature | Composio | LangChain | OpenAI Agent Builder | Salesforce Agentforce | ServiceNow AI Agents | Microsoft Copilot Studio | UiPath Agent Builder | CrewAI | Google Vertex AI | Amazon Bedrock |
|---|---|---|---|---|---|---|---|---|---|---|
Primary Use Case | Agent Action & Integration Layer | Visual Agent Builder & LLM Intelligence | CRM/Sales Automation | IT/Employee Workflows | Employee Productivity & M365 Copilots | Legacy System & Process Automation | Multi-Agent Collaboration | |||
Open Source | No | Yes | Partial (Agents SDK only) | No | No | No | No | No (Integrates with open-source tools) | No (Provides access to open-source models) | |
Managed Authentication (for end-users) | Yes | No (Developer builds manually) | No | Partial (Salesforce only) | Partial (Internal ACLs) | Partial (Internal ACLs) | Partial (Internal ACLs) | No (Developer builds manually) | Partial (Via Google ecosystem) | Partial (Via AWS ecosystem) |
No. of Pre-built Tools | 500+ | Varies (Community-driven, manual setup) | 8 native + MCP ecosystem** | Limited (Salesforce / MuleSoft) | Limited (Integration Hub) | 1,000+ (Power Connectors)** | High | Varies (Community-driven, manual setup) | Limited (Primarily Google services) | Limited (Primarily AWS services) |
Handles Token Refresh | Yes (Automated & Managed) | No (Requires custom implementation) | No | Internal only | Internal only | Yes | Yes | No (Requires custom implementation) | Partial (Within GCP) | Partial (Within AWS) |
Event-Driven Triggers | Yes | No (Requires custom code) | No (Conversation-First) | Yes | No (Conversation-First) | Yes | Yes | No (Requires custom code) | Yes (Via Google Cloud Functions) | Yes (Via AWS Lambda) |
Framework Agnostic | Yes | Yes | No | No | No | No | No | Yes | No | No |
Pricing Model | SaaS / Usage | Open Core | Pay-as-you-go | Consumption (Flex Credits) | Consumption (Flex Credits) | Subscription + Consumption | Unified Consumption | Open Core | Pay-as-you-go | Pay-as-you-go |
*Note on OpenAI: MCP ecosystem claims 8,000+ apps, but write operations remain limited or buggy on many servers. The platform is still in alpha/beta as of December 2025.
**Note on Microsoft: While Microsoft offers many connectors, many external (non-Microsoft) connections require "Premium" licensing and don't handle external end-user OAuth as smoothly as native internal flows.
Conclusion: Stop Building Plumbing, Start Building Value
Building a production-ready AI agent requires more than a powerful "brain" like LangChain or Vertex AI. The bottleneck separating prototype from product remains the "body," a secure, scalable action layer.
By abstracting away the complexities of integration, authentication, and execution, developers can shift focus from building brittle plumbing to shipping agents that deliver tangible business value. Reliably connecting logic to the real world matters as much as the logic itself.
Explore our 500+ toolkits or sign up for a free developer account to connect your agent in minutes.
Frequently Asked Questions
Is Composio an Agent Builder platform?
No. Agent builders like LangChain or CrewAI provide the "brain" for an agent's logic. Composio provides the essential "body." We function as an agent-native Action and Integration Platform that handles complex plumbing, including managed authentication and LLM-ready tools. This allows any agent "brain" to connect to real-world applications and perform meaningful tasks securely and reliably.
How does Composio integrate with AI Agent Builders like LangChain, CrewAI, Salesforce Agentforce and ServiceNow Agent Orchestrator?
Composio is framework-agnostic and functions as a modular component for any agent stack. Your agent "brain," whether built with LangChain, CrewAI, or OpenAI, can call upon Composio's 500+ pre-built tools to execute actions. We provide a secure, authenticated connection to apps like Salesforce or Jira, allowing your agent to focus on orchestration, not plumbing.
Can I use an Agent Builder, such as LangChain or CrewAI, without manually building integrations?
Yes. LangChain and CrewAI handle orchestration logic, but they leave authentication, credential management, and API formatting to developers. Composio plugs directly into both frameworks as the action layer, providing 500+ pre-built, LLM-ready tools with managed authentication. Your agent code stays focused on logic while Composio handles the integration plumbing, including OAuth flows, token refreshes, and secure credential storage for your end-users.
How do I connect an AI agent to Salesforce, Jira, or Gmail?
You have two options. You can build the integration yourself, which means implementing OAuth flows, managing API credentials, handling token refreshes, and formatting requests for each application. For a single tool, this can take weeks of engineering work.
Or you can use Composio's pre-built toolkits. Composio provides LLM-ready connectors for Salesforce, Jira, Gmail, and 500+ other applications. The platform handles authentication and credential management for your end users, so your agent can execute actions such as updating a Salesforce record, creating a Jira ticket, or sending an email within minutes of setup.
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