Building an AI agent means connecting a model to tools and giving it a controlled way to decide what happens next. This checklist takes you from a narrow task to a supervised deployment, with practical checks for permissions, failures, evaluation, and cost.
How Do You Build a Safe AI Agent?
Start with one measurable task and the smallest possible set of tools. Put the model inside a software-controlled loop that validates tool requests, records results, and stops at explicit limits. Require approval for consequential actions and evaluate the whole workflow, not just the quality of its final message.
1. Define the job before choosing a framework
Why: A broad instruction such as “manage customer support” provides no reliable boundary for success. An agent needs an operational contract: what it may read, what it may change, and when it must hand work to a person.
How: Write a one-page specification containing the input, expected output, authorized sources, forbidden actions, and completion condition. A sensible first project is “prepare a support response using approved help articles,” not “resolve every customer problem.” Keep the first version read-only or draft-only.
Define success in observable terms. A completed draft should cite the relevant article, identify missing information, and avoid promising refunds. Separately record unacceptable outcomes, such as exposing another customer's details. A response can look polished while failing every operational requirement.
Before adopting an agent, ask whether a fixed workflow would suffice. If every request follows the same sequence, conventional code with one model call may be easier to debug. Anthropic distinguishes predefined workflows from agents that dynamically choose their process and tool use in its architecture guide.
Framework selection comes after that decision. Consider whether a framework exposes tool calls, state transitions, retries, and traces clearly. A small custom loop can be adequate; a framework is useful when its abstractions simplify real needs rather than hide them.
2. Design tools as narrow interfaces
Why: The model's instructions are not an access-control system. A tool should make unauthorized operations impossible at the application layer rather than merely telling the model not to attempt them.
How: Offer specific operations such as lookup_help_article(article_id) instead of a general database console. Define required arguments, accepted types, output fields, error responses, and authorization checks. Validate arguments on the server before using them.
OpenAI's function-calling documentation explains the separation between a model requesting a tool call and application code executing it. Treat that separation as a security boundary. Structured arguments can make requests easier to parse, but they do not prove that a requested action is appropriate.
| Tool | Allowed behavior | Application-enforced boundary |
|---|---|---|
| Search help articles | Retrieve approved support content | Approved index only |
| Read ticket | Return the current ticket | Account and ticket authorization |
| Create draft | Save a proposed response | No direct sending permission |
| Request approval | Queue an action for a reviewer | Approval tied to the exact action |
Keep credentials out of prompts and tool responses. Use a managed secret store where available, or protected environment configuration for a prototype. Do not commit credentials, print them in traces, or give a tool a powerful account when a restricted service identity would work.
3. Build a bounded execution loop
Why: Agents can repeat unsuccessful actions, misunderstand partial results, or spend resources without finishing. Reliable execution requires a state machine controlled by software, not just a request to “be efficient.”
How: For each run, initialize a task record and budget. Send the model its instructions and permitted tools, inspect its next requested action, validate the request, execute it if authorized, and return the result. Stop when the completion condition is met or a limit is reached.
Maintain explicit states such as running, awaiting approval, completed, failed, and cancelled. Record which tool calls succeeded before a failure. This prevents a restarted run from unknowingly repeating a side effect.
Set separate limits for elapsed time, model calls, tool calls, and spending. A hypothetical prototype might allow five tool calls and one reformulated search before escalating. These are design examples, not universal thresholds. Choose limits from the task's complexity and failure consequences.
Handle failures by category. A temporary network error may justify a bounded retry. Invalid credentials need operator intervention. An empty search result might justify one broader query, followed by an honest statement that the evidence is missing. Never instruct an agent to keep trying indefinitely.
Use idempotency keys or equivalent duplicate protection when tools create records. A timeout does not necessarily mean the remote action failed; check the external system before retrying a write.
4. Put approvals and untrusted data outside the model's discretion
Why: Retrieved pages, messages, and documents can contain instructions intended to redirect the agent. OWASP describes prompt injection and excessive agency among important LLM application risks.
How: Treat external text as evidence to inspect, not as authority to change permissions. Separate task instructions from retrieved content and restrict which destinations tools can contact. Do not let a document grant the agent access to another account or authorize a payment.
Require an approval gate for sending messages, deleting records, spending money, or changing access. Show the reviewer the exact proposed action, destination, supporting evidence, and material consequences. If the agent changes the action after approval, require a new approval.
Provide a cancel control and a way to revoke the service identity quickly. Reviewers should know who owns the run and where unresolved work goes. Human oversight is ineffective if approval requests are vague or arrive faster than people can examine them.
Keep sensitive material out of unnecessary logs. Store enough information to diagnose the action without indiscriminately copying customer documents into every monitoring system. Apply retention and access rules to traces as well as primary data.
5. Test a complete run with a worked example
Why: A good answer on one friendly prompt says little about tool reliability, authorization, or recovery from malformed input.
How: Build a small evaluation set before changing prompts repeatedly. Include ordinary requests, missing documentation, conflicting sources, tool outages, unauthorized account references, and malicious instructions embedded in retrieved text. Define expected behavior for each case.
Consider a hypothetical support agent handling a request about exporting a report. It reads the current ticket, retrieves an approved export article, and saves a draft with the article link. It does not send the email. If the article applies only to an older product version, the correct outcome is an escalation or clarification, not a confident workaround.
Suppose a hypothetical evaluation contains 40 cases: 25 routine questions, 10 ambiguous requests, and five adversarial documents. Record task completion separately from unsafe-action attempts. An average quality score can conceal one serious permission failure, so critical safety conditions should be explicit release blockers.
Inspect traces when cases fail. Was the source missing, the tool response unclear, the model mistaken, or the approval boundary absent? Fix the relevant layer instead of adding every correction to an increasingly long prompt.
6. Release gradually and own the maintenance
Why: A prototype operates in a stable environment; production faces changed APIs, outdated documents, unusual users, and unexpected traffic.
How: Start in shadow mode, where the agent proposes actions without executing them. Move to supervised use for a limited queue only after reviewing results. Expand permissions separately from expanding volume, so an increase in workload does not silently increase authority.
Monitor completion, escalation, latency, retries, cost per completed task, and permission violations. Assign someone to review failures and update the evaluation set. Re-run relevant cases after model, tool, prompt, or knowledge-source changes.
Keep a rollback path that routes requests to the existing manual process. Document how to disable writes, pause new runs, and resolve runs already awaiting approval. An agent is a maintained service, not a prompt you finish once.
Questions About Building AI Agents
Do I need multiple agents for the first version?
Usually not. Start with one agent and explicit tools. Add separate agents only when you can identify a distinct responsibility and show that the handoff improves your evaluation results enough to justify extra complexity.
Should I store the agent's entire conversation forever?
No. Retain what the task and audit requirements justify. Separate temporary conversation context from durable business records, and apply deletion and access policies to both.
What should happen when a spending limit is reached?
Stop further paid actions, preserve the task state, and report what is complete and what remains unresolved. Increasing the budget should require an authorized decision rather than an agent's own request.
Start Small and Control Each Run
A useful agent is a bounded service with measurable outcomes, limited permissions, and a clear fallback. Start small enough that every tool call and failure is understandable.
Next steps: Review what autonomous agents are, compare agent platforms, and plan your AI evaluations.
Sources
- Anthropic — Building effective agents
- OpenAI — Function calling
- OWASP — Top 10 for Large Language Model Applications
This guide is general education, not professional advice.