What tools can I use to build an AI agent?

Summary

• Popular frameworks for building AI agents include LangChain, CrewAI, AutoGen, and OpenAI Agents SDK, each offering distinct approaches to tool use, memory, and multi-agent orchestration.
• When choosing an agent platform, prioritize model flexibility, governance, contextual grounding in enterprise data, built-in evaluation, and production lifecycle support.
• Databricks Agent Bricks provides a unified control plane to build, run, and govern AI agents across any model or framework, with self-improving evaluation and enterprise-grade governance.

What tools can I use to build an AI agent?

Building an AI agent requires choosing the right combination of frameworks, platforms, and infrastructure. Whether you need a single chatbot or a fleet of specialized agents collaborating on complex workflows, more tools exist now than ever. The challenge is selecting tools that work together, scale to production, and remain governed as your agent portfolio grows. As organizations pursue AI transformation, having the right agent-building strategy becomes critical.

Key frameworks for building AI agents

LangChain is a widely used open-source framework for building AI agents and LLM applications, with roughly 134k GitHub stars and more than 1,000 pre-built integrations. It provides modular components for prompt management, tool use, memory, and retrieval-augmented generation.
CrewAI is a multi-agent orchestration framework built around a role-based model. Each agent has a defined persona, tools, and a specific task within a larger crew. Its abstractions make it straightforward to get a working multi-agent prototype running quickly.
AutoGen, released by Microsoft Research in 2023, uses conversation-driven multi-agent collaboration. Agents exchange code, critique proposals, and iterate in structured dialogue, useful for research, analysis, and code-generation workflows.
Other notable options include:

• OpenAI Agents SDK: Released in March 2025, this production-grade toolkit replaced the experimental Swarm framework with built-in tool use, handoffs, and guardrails.
• Anthropic Claude Agents: Anthropic offers its own agent SDK alongside Claude models for building agentic workflows.
• GCP Vertex AI Agent Builder: Google provides agent-building capabilities within its Vertex AI platform.
• Amazon Bedrock Agents: AWS offers agent tooling integrated with its Bedrock model service.
• Azure AI Agent Service: Microsoft provides agent capabilities through Azure AI Foundry.

Single-agent vs. multi-agent architectures

Choosing between a single agent and a multi-agent system depends on task complexity and reliability requirements.

Factor	Single-agent	Multi-agent
Complexity	Simpler to build and debug	Better for workflows with distinct subtasks
Specialization	One model handles everything	Each agent can use a different model or tool set
Failure isolation	One failure can halt the workflow	Failures are contained to individual agents
Coordination overhead	None	Requires orchestration logic and message passing

For most teams, starting with a single agent and decomposing into multiple agents as complexity grows is the practical path. You can build compound AI systems on Databricks to support this evolution.

What to look for in an AI agent platform

When evaluating platforms, prioritize these five capabilities:

1. Model flexibility: Can you use any LLM, open-source or proprietary, without lock-in?
2. Governance: Does the platform enforce access controls, lineage tracking, and policy enforcement? An AI governance framework is essential for enterprise readiness.
3. Contextual grounding: Can agents reason over your enterprise data, not just general knowledge?
4. Evaluation: Are there built-in benchmarks and quality loops, or do you rely on ad-hoc spot-checks?
5. Production lifecycle: Does the platform handle deployment, monitoring, and continuous improvement?

How to add memory and tool use to an agent

Most frameworks support two critical capabilities: memory and tool use.

• Memory lets agents retain context across turns or sessions. Short-term memory holds the current conversation. Long-term memory persists facts across sessions using vector stores or databases.
• Tool use lets agents call external APIs, query databases, run code, or search the web. Frameworks like LangChain and OpenAI Agents SDK provide standard interfaces for defining and registering tools.

Both capabilities are essential for agents that go beyond simple question-answering.

How Agent Bricks addresses the full agent lifecycle

Agent Bricks is the unified control plane to build, run, and govern AI agents across any model, provider, or framework, eliminating sprawl through centralized management. It rests on three pillars:

• Open and governed: Build with any AI model (OpenAI, Gemini, Llama, Anthropic) and any framework while maintaining enterprise governance, including granular access controls, lineage tracking, cost controls, and policy enforcement. The AI Gateway serves as the governance layer for agentic AI.
• Contextual reasoning: Built natively into the Databricks Platform, Agent Bricks gives agents deep semantic understanding of enterprise data through learned business context.
• Self-improving: Agent Bricks builds benchmarks using your own data, evaluates every output against them, and leverages prompt optimization, fine-tuning, and RLHF to automatically improve accuracy without costly rebuilds.

FAQs

What are the most popular frameworks for building AI agents from scratch?

Popular frameworks include LangChain, CrewAI, AutoGen, OpenAI Agents SDK, and Anthropic Claude Agents. For enterprise-scale agent management, Agent Bricks provides a unified control plane that works with any of these frameworks while adding governance and evaluation.

How do I build an AI agent using LangChain and what are its key components?

LangChain provides modular components, including prompt templates, chains, agents, memory modules, and tool integrations, that you compose into an end-to-end agent pipeline. You define a reasoning loop, connect tools, and add retrieval-augmented generation for grounded responses.

What is CrewAI and how does it help with multi-agent AI systems?

CrewAI is an open-source framework that lets you define agents with specific roles, assign tasks, and coordinate their work through structured workflows. It is well-suited for rapid multi-agent prototyping.

How do I use AutoGen to create collaborative AI agents?

AutoGen uses a conversation-based paradigm where you define multiple agents, assign them roles, and let them collaborate through structured message exchanges. Agents can generate code, review outputs, and iterate until they reach a satisfactory result.

What features should I look for when choosing an AI agent development framework?

Prioritize model flexibility, built-in governance, contextual grounding in your data, evaluation and benchmarking tools, and production deployment support.

How do I build a custom AI agent using OpenAI's assistants API?

The OpenAI Agents SDK (which succeeded the Assistants API for agentic use cases) lets you define agents with instructions, attach tools like code interpretation and file search, and manage conversation threads with built-in guardrails.

What are the best open-source tools for building autonomous AI agents?

LangChain, CrewAI, and AutoGen are among the most actively maintained open-source options. Each supports tool use, memory, and multi-step reasoning out of the box.

How do I add memory and tool-use capabilities to an AI agent?

Add short-term memory through conversation buffers and long-term memory via vector stores. Register tools, APIs, databases, code executors, using your framework's tool interface so the agent can call them during reasoning.

What is the difference between single-agent and multi-agent AI architectures?

A single-agent architecture uses one model to handle the full task. Multi-agent architectures assign subtasks to specialized agents that collaborate, improving failure isolation and enabling different models per role.

How do I deploy and monitor AI agents in a production environment?

Production deployment requires serving infrastructure, access controls, evaluation pipelines, and continuous monitoring. Agent Bricks handles this with Model Serving, Unity Catalog governance, LLM Judges for automated evaluation, and Agent Learning Human Feedback for ongoing accuracy improvement.

Bring your AI agents to production

Building AI agents is only the first step. Running them at enterprise scale requires governance, contextual grounding, and continuous quality improvement. Agent Bricks eliminates agent sprawl with a unified control plane, letting you build with any model and framework while keeping every agent governed and improving over time.
Explore Databricks AI to start building and governing your agents at scale.