What an AI agent is
An AI agent is software that uses a language model to decide what to do next, take an action through a tool, observe the result and repeat until a goal is met. A chatbot answers; an agent acts.
1. Define the job, not the technology
Write down the single workflow the agent owns, what a successful outcome looks like, which systems it must read or change, and what it must never do. "Resolve password-reset tickets end to end" is buildable; "an AI assistant for operations" is not. Check whether a fixed workflow with one model call would do the job — if the steps never change, you may not need an agent at all.
2. Choose your model(s)
Start with a strong hosted model from a provider such as Anthropic (Claude), OpenAI (GPT) or Google (Gemini), and confirm it handles your task reliably before optimising. Open-weight models are a good fit when data must stay in your environment or volume justifies self-hosting. Many production agents use more than one model: a capable one for planning and a smaller, cheaper one for routine steps.
3. Give it tools through function calling
Tools are how an agent acts: search a knowledge base, look up an order, create a ticket, send an email. Each tool needs a clear name, a precise description and a strict input schema — the model chooses tools from those descriptions, so vague ones cause wrong calls. Keep the toolset small, make tools return concise, structured results, and validate every input on your side before executing.
4. Connect tools and data with MCP
The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data sources. Wrapping a system — your CRM, database or file store — in an MCP server lets any MCP-compatible agent or client use it without a bespoke integration each time. Treat MCP servers like any API: authenticate them, scope what they expose, and log every call.
5. Add retrieval (RAG) where knowledge matters
If the agent needs your policies, documentation or product data, use retrieval-augmented generation: index your content, retrieve the relevant passages at run time and give them to the model with instructions to rely on them. Retrieval quality — chunking, metadata, freshness and access control — usually matters more than the model. See our RAG development service for how we approach it.
6. Design memory and state
Decide what the agent remembers within a task (the conversation and tool results), across sessions (user preferences, case history) and what it must forget. Store durable state in your own database rather than relying on an ever-growing prompt, and make long-running tasks resumable so a restart does not lose work.
7. Set guardrails and permissions
- Least privilege: give each tool only the access the job needs, using the user's permissions where possible.
- Separate read and write tools, and require confirmation for irreversible actions.
- Treat content from web pages, emails and documents as untrusted, since it can contain prompt-injection attempts.
- Cap steps, tokens and spend per task, and fail safely when a tool or API is down.
8. Keep a human in the loop
Route high-impact actions — refunds, account changes, outbound messages — to a person for approval, and give users an easy way to escalate. Widen autonomy only as evaluation results earn it.
9. Evaluate before you ship
Build a test set of realistic tasks, including edge cases and adversarial inputs, with the expected outcome for each. Score task success, tool-call correctness, safety and cost per task, and rerun the suite whenever you change a prompt, tool or model.
10. Add observability and cost control
Trace every run end to end — prompts, tool calls, results, latency and tokens — so you can debug failures and spot regressions. Track cost per completed task, use prompt caching where supported, and route easy steps to smaller models. Our AI agent development cost guide explains typical build and run budgets.
11. Deploy, monitor and iterate
Release to a small group first, behind a feature flag, with alerting on error rates and unusual spend. Review flagged conversations regularly, add real failures to your evaluation set, and improve in small, measured steps.
Build your agent with us
PixoBots designs and builds production AI agents with the evaluation, guardrails and monitoring to run safely. Explore our AI agent development services, hire AI agent developers for your own team, or tell us about your workflow.