1. What is an AI agent?
An AI agent is a system where a large language model decides what to do next, calling tools and retrieving information until a goal is reached. The difference between a chatbot and an agent is the loop: a chatbot answers, an agent acts, observes the result, and decides again. Agents plan, route, retrieve, write, and execute. They can summarize a document, query a database, file a ticket, draft an email, then check its own work.
The right mental model is a junior employee with permanent memory, infinite patience, and zero judgement of their own. Your job is to give them a clear role, the right tools, the right information, and a way to measure their work.
2. Define the job to be done
Before any code, write the agent's job description in one sentence. Who does it serve, what task does it complete, what does success look like? "An assistant for support engineers that drafts a verified response to a customer ticket using our knowledge base and recent product release notes" is a job. "An AI agent for our company" is not.
Then write three example inputs and the ideal outputs. These become your first evaluation set. If you can't write them, the agent isn't ready to be built.
3. Choose the architecture
Three patterns cover 95% of enterprise agents:
One prompt, one response. Use for summarization, classification, and content rewriting.
One LLM that can call retrieval, APIs, and functions in a loop. Default for almost every business case.
Planner, researcher, critic, executor coordinating. Powerful, but doubles the failure surface.
Start at the simplest pattern that passes your evaluation. Upgrade only when it fails.
4. Model and prompt
Pick a base model first. Capable models (Claude Sonnet, GPT-class, Mistral Large) handle complex tool use; cheaper models work well for narrow agents. Route per task: a cheap model for retrieval planning, a capable model for final generation.
Write the system prompt as a role plus rules, not a story. Define who the agent is, what it must always do, what it must never do, and what format the output must take. Constrain outputs with JSON schemas where possible. Structured output is the single biggest reliability win in agent design.
5. Tools and memory
Tools are the agent's hands. Each tool needs a tight description, an input schema, and an output schema. The model only uses a tool well when its description explains when to call it, not just what it does. Start with three tools: search your knowledge, look up a record, perform one action. Add more only when evaluation demands it.
Memory has three flavors: short-term (conversation context), task memory (state across the current loop), and long-term (facts about a user or organization that should persist). Be explicit about which you need. Most agents don't need long-term memory and adding it introduces leakage and staleness risks.
6. Ground in your knowledge
For any enterprise agent, retrieval is the difference between confident hallucination and verifiable answers. Build a retrieval layer over your real sources: docs, wikis, tickets, databases, with hybrid search (vector + keyword), re-ranking, and document-level permission filtering. Every claim the agent makes should be backed by a retrieved source with a clickable citation.
If your retrieval is bad, no amount of prompt engineering will save the agent. Test retrieval in isolation before you test the agent.
7. Evaluate before you ship
A golden-set evaluation harness is not optional. Build a small set of 20–100 representative inputs with expected behaviors. Score every change on: groundedness (does the answer match the source?), accuracy (is it correct?), refusal correctness (does it decline when it should?), latency, and cost. Run the harness on every prompt or model change.
Layer in LLM-as-judge for subjective dimensions, but always keep a human spot-check loop. Numbers without sampled examples lie.
8. Deploy with guardrails
Production is where most agent projects quietly die. Ship with:
- Observability on every loop: inputs, tool calls, retrieved chunks, model output, cost, latency.
- Rate limits and budget caps per user, per tenant, per tool.
- SSO, RBAC, and IP allowlists. The agent inherits the user's permissions, never elevates them.
- Human-in-the-loop review for sensitive actions. Default to suggest, not execute.
- Audit logs that an auditor can read, with per-request traces.
9. Common mistakes to avoid
- Skipping evaluation because "it looks good in the demo."
- Too many tools: confused agents make bad choices.
- Ignoring permissions until the first incident.
- Multi-agent everything when one agent with tools works better.
- Free-form output when a schema would catch 80% of failures.
- One mega-prompt that nobody can debug six months later.