How to Build an AI Agent

Bottom line: Build an AI agent by defining a narrow task, giving it the right tools, adding memory, choosing a planning strategy, and wrapping everything in a framework that can run in production.

Step 1: Define the task

The most common mistake is giving an agent a vague goal like "help me with sales." Start with a narrow, repeatable task with clear inputs and outputs. Examples:

• Read support tickets, look up account status, draft a response.
• Search research papers, summarize three findings, cite sources.
• Fetch daily revenue, compare to last week, post a Slack summary.

Write down the success criteria. What does "done" look like? The clearer your criteria, the easier it is to evaluate and debug the agent.

Step 2: Choose tools and data sources

Agents are only useful if they can interact with the world. List every external system the agent needs:

• Data: databases, files, APIs, vector stores, search engines.
• Actions: send email, create tickets, run code, call webhooks.
• Knowledge: documents, wikis, past conversation logs.

Wrap each capability as a tool with a clear name, description, and input schema. If you use MCP, you can reuse existing servers; otherwise, write custom functions.

Step 3: Add memory

Memory turns a chatbot into an agent. Decide which of these you need:

• Short-term memory: the current conversation, usually sent as message history.
• Long-term memory: facts about the user or task, persisted across sessions.
• Retrieval memory: relevant documents pulled from a vector database or search index.

For most production agents, short-term memory is required, long-term memory is valuable, and retrieval memory is needed when the agent must reason over private documents.

Step 4: Implement planning

Planning is how the agent decides what to do next. Pick a strategy that matches task complexity:

Step 5: Pick a framework

You do not need a framework for simple agents, but one helps as complexity grows:

• LangGraph - best for stateful, graph-based workflows.
• CrewAI - best for role-based crews and business automation.
• PydanticAI - best for type-safe Python agents.
• AutoGen - best for conversational coding and research teams.

Compare all major frameworks in our Agent Framework Comparison.

Step 6: Deploy and monitor

Production agents need more than working code. Make sure you have:

• Observability: trace every tool call, decision, and error.
• Error handling: retry failed tools, surface clear messages, and fail gracefully.
• Sandboxing: limit what tools can do, especially if they run code or access private data.
• Human oversight: add approval steps for high-stakes actions like sending money or emails.
• Feedback loops: collect user corrections and use them to improve prompts and tool definitions.

A minimal agent example

{agentCode}

Common pitfalls

• Too many tools. Start with 2-3. More tools mean more confusion and higher latency.
• Unclear tool descriptions. The model relies on descriptions to pick tools. Make them specific.
• No retries. APIs fail. Plan for retries, fallbacks, and graceful degradation.
• Over-autonomy. Let the agent handle low-risk tasks; require approval for high-risk ones.
• Ignoring latency. Each tool call adds time. Design workflows to minimize round trips.

Published 2026-06-12