Do I need a framework to build an AI agent?

No. A basic agent is a loop that calls a model, executes tool calls, and feeds results back, which takes little code. Frameworks and SDKs add features such as state management, tracing, and multi-agent coordination, which help as agents grow more complex.

What tools should an AI agent have?

Only those the task needs. A documentation agent might need search and read tools; a ticket triage agent might need read ticket, search knowledge base, and propose label. Fewer, clearly described tools make the agent more reliable and cheaper to run.

How do I stop an AI agent from making mistakes?

You cannot remove mistakes entirely, but you can contain them: narrow scope, clear tools, retrieval for accurate facts, step and spend limits, least-privilege permissions, human approval for important actions, and regular evaluation on real examples. Review failures regularly and fix the cause, whether tools, retrieval, or instructions.

How to Create an AI Agent

To create an AI agent, define one specific task and what success looks like, choose a model, give it a few clearly described tools, and build a loop that calls the model, runs tool calls, and feeds results back. Add retrieval and memory for knowledge, set guardrails such as permissions and step limits, then test on real examples.

Step 1: define the task

Pick a specific job, such as triaging support tickets, answering questions from internal documentation, or drafting pull request descriptions. Write down inputs, expected outputs, and how you will know the agent succeeded. Narrow tasks are easier to build, test, and trust than general assistants.

Step 2: choose a model

Choose a model that handles the task's reasoning and tool use reliably. Larger hosted models are strongest at complex multi-step tasks; smaller or local models can handle simple, well-structured jobs cheaply. Start with a capable model to prove the workflow, then test cheaper options.

Step 3: design the tools

Tools are functions the agent can call: search documents, read a ticket, run a query, send a draft for approval. Give each a clear name, a description written for the model, and a strict input schema. Start with the minimum set. You can expose tools directly through function calling or through MCP servers so they are reusable across clients.

Step 4: build the loop

The core of an agent is a loop: send the task, context, and tool definitions to the model; if it returns a tool call, execute it and append the result; repeat until it returns a final answer or hits a limit. Frameworks and SDKs, such as the Claude Agent SDK, the OpenAI Agents SDK, and LangGraph, provide this loop with extras, but a minimal version is a few dozen lines.

Step 5: add knowledge through retrieval

Agents need facts: product details, policies, code, past decisions. Instead of stuffing everything into the prompt, index the knowledge and give the agent a search tool, or retrieve relevant chunks automatically before each call. Retrieval keeps context small, improves accuracy, and lowers cost.

Step 6: add memory

For tasks that span sessions, store useful results, decisions, and user preferences, and retrieve them later. Keep memory structured and prune it, since stale memory misleads the agent.

Step 7: set guardrails

Limit the number of steps, tool calls, and tokens per task. Use least-privilege credentials. Require human approval for irreversible or external actions. Treat content from the web, emails, and documents as untrusted. Log every action with the agent's identity.

Step 8: test with real examples

Collect real inputs with known good outputs. Run the agent on them, review results, and record failures. Change one thing at a time, such as a tool description or retrieval setting, and rerun. Track success rate, cost per task, and time per task.

Step 9: deploy and monitor

Run the agent in an isolated environment with logging and alerts. Start with a small group of users or a shadow mode where people review outputs before they take effect. Watch for new failure types and expand scope only when results are consistently good.

Where first agents fail

Typical mistakes include starting with a general-purpose assistant, giving the agent dozens of tools, putting all knowledge in the system prompt, skipping evaluation, and relying on prompt instructions for safety instead of permissions. Each makes the agent less reliable or more expensive.

Single agent or multiple agents

Start with one agent. Multi-agent setups, where specialised agents hand work to each other, can help with large tasks but add coordination complexity, cost, and new failure modes. Split into multiple agents only when a single agent's context or tool set becomes unmanageable, and give each a clear role and interface.

Writing the system prompt

Describe the agent's role, the task, the tools and when to use each, the output format, and what to do when uncertain, such as asking for clarification or escalating to a person. Keep it concise. Put changing knowledge in retrieval rather than in the prompt, so the prompt stays stable and cacheable.

Estimating cost

Agent cost is roughly the tokens per step multiplied by steps per task multiplied by tasks per day. Long contexts and many steps add up quickly. Retrieval, short system prompts, prompt caching for stable prefixes, and step limits keep cost predictable. Measure actual cost per task during testing.

When not to build an agent

If a task follows fixed steps with no judgement, a script or workflow is simpler and more reliable. Agents add value when tasks vary, need interpretation, or require choosing between tools.

Iterating after launch

Collect failed cases from production, add them to your test set, and fix causes one at a time. Most improvements come from better tool descriptions, retrieval, and narrower scope rather than larger models.

Frequently asked questions

Do I need a framework to build an AI agent?: No. A basic agent is a loop that calls a model, executes tool calls, and feeds results back, which takes little code. Frameworks and SDKs add features such as state management, tracing, and multi-agent coordination, which help as agents grow more complex.
What tools should an AI agent have?: Only those the task needs. A documentation agent might need search and read tools; a ticket triage agent might need read ticket, search knowledge base, and propose label. Fewer, clearly described tools make the agent more reliable and cheaper to run.
How do I stop an AI agent from making mistakes?: You cannot remove mistakes entirely, but you can contain them: narrow scope, clear tools, retrieval for accurate facts, step and spend limits, least-privilege permissions, human approval for important actions, and regular evaluation on real examples. Review failures regularly and fix the cause, whether tools, retrieval, or instructions.