Building an AI agent comes down to four things: a clear job, a language model, the tools and data it can use, and the guardrails that keep it safe. Demos stop at the first three. Agents that run in production on real company data need the fourth, which means permissions, approvals, evaluation and monitoring. The eight steps below cover all of it.
What an AI agent is made of
- Model: the LLM that reasons and decides what to do next.
- Instructions: the job description, rules and output format.
- Tools: functions the agent can call, such as querying a database, updating a record or sending a message.
- Memory and context: task state, history and relevant knowledge.
- Guardrails: permissions, approvals, limits and logging.
How to build an AI agent in 8 steps
Step 1: Define one job with a measurable outcome
"Help support" is not a job. "Triage new tickets by urgency and topic, attach the customer's order history and route to the right queue" is. Write down the trigger, the expected output and how you will measure success, such as routing accuracy or time saved.
Step 2: Choose the model
Start with a capable general model for reasoning and tool use, then test whether a smaller, cheaper model handles the task well enough. Consider context length, latency, cost per run and data residency.
Step 3: Connect data and tools
List the actions the agent needs, and nothing more: "read orders for customer X", "set ticket priority", "draft a reply". Each tool should have a clear name, description and input schema. Connect to the systems the data already lives in instead of copying it somewhere new.
Step 4: Write the instructions
Describe the role, the goal, the step-by-step approach, rules ("never promise refunds over $100"), what to do when unsure ("ask a human") and the output format. Include two or three examples of good output.
Step 5: Add memory and context
Give the agent the context it needs per task, such as the customer record, related tickets or policy documents, through retrieval or tool calls. Store task state if the agent works across multiple steps or sessions.
Step 6: Set permissions and approvals
Scope what the agent can read and change to specific rows, fields and actions. Keep credentials out of the agent's reach. Put human approval in front of irreversible actions like payments, deletions and external messages. See RBAC for AI agents.
Step 7: Evaluate before launch
Build a test set of 50–200 real past cases with known correct outcomes. Measure accuracy, failure types and cost. Include tricky cases and prompt-injection attempts. Run in "suggest only" mode alongside people before letting the agent act.
Step 8: Deploy, monitor and improve
Log every run: inputs, tool calls, outputs, cost and outcome. Review failures weekly, add them to your test set, and adjust instructions or tools. Expand the agent's autonomy only as its track record earns it.
Build approaches compared
| Approach | Examples | Good for | Trade-off |
|---|---|---|---|
| Code frameworks | LangGraph, OpenAI Agents SDK, Claude Agent SDK | Engineering teams wanting full control | You build auth, permissions, UI and hosting |
| Automation tools with AI steps | n8n, Zapier, Make | Adding AI to existing integrations | Limited interfaces for human review |
| Platforms for agents on business data | Jet Admin | Agents that act on company data with approvals and apps around them | Less suited to agents unrelated to business data |
Common mistakes
- Starting with a vague, company-wide agent instead of one job.
- Giving the agent admin credentials.
- Relying on prompt instructions for security.
- Skipping evaluation and going straight to production.
- No interface for people to review, correct and approve agent work.
Build AI agents with Jet Admin
Jet Admin lets you build agents that work on your data across 200+ integrations, from PostgreSQL and Snowflake to Salesforce, Stripe and Slack, queried in place. Describe the agent's job, connect the tools it needs, and run it inside the same permission model as your apps. Workflows add approvals before sensitive actions, and dashboards and review queues give your team an interface to check agent work. Granular permissions, SSO and audit logs are on Business and above. For more, read our AI agent builder guide and AI agent examples.
Frequently asked questions
How do I build an AI agent?
Define one job, choose a model, connect the tools and data it needs, write clear instructions, add context, set permissions and approvals, evaluate on real cases and deploy with monitoring.
Can I build an AI agent without coding?
Yes. No-code and low-code platforms let you configure tools, instructions and approvals visually, while developers can still add code where needed.
How long does it take to build an AI agent?
A prototype can take hours. A production agent with permissions, evaluation and monitoring usually takes a few weeks.
What is the hardest part of building an AI agent?
Making it reliable and safe on real data: evaluation, permissions and handling the cases it gets wrong.
Build your first agent on your own data
Start with Jet Admin for free and build an agent with permissions and approvals built in.