Agentic AI & agents
How to Build an AI Agent
From a single tool call to a reliable multi-step agent, without unnecessary complexity.
Building a first AI agent is far more approachable than the term suggests, if you build it up in deliberate stages instead of reaching for a complex framework on day one.
This walks through a practical build order: start with a single tool call, add planning, add memory and limits, then add evaluation — each stage adding real capability you can test before moving on.
Key takeaways
Step 1: one tool, one reliable call
Before building any planning loop, get comfortable with the fundamentals: define one tool (say, a search function), have the model decide when to call it, and confirm the model can use its result correctly in a final answer.
This sounds trivial but surfaces real issues early — unclear tool descriptions, malformed inputs, and error handling gaps — that would be much harder to debug inside a full agent loop.
Step 2: add the planning loop
Once single tool use is solid, wrap it in a loop: let the model decide, after each tool result, whether to call another tool, ask for clarification, or finish. Add a maximum step count immediately — this is not optional.
Test with tasks that genuinely require multiple steps, not just single-step tasks dressed up as multi-step ones, to confirm the loop is adding real value.
Step 3: memory, limits, and evaluation
Add memory so the agent can reference earlier steps without re-deriving them. Add hard limits on cost and step count as a safety net, not just a soft guideline. Then build a small evaluation set of realistic tasks with known good outcomes, and run it every time you change the agent.
This staged approach — tool, then loop, then safety and evaluation — consistently produces more reliable agents than starting from a complex framework template.
Put this into practice