How to Build an AI Agent: Architecture, Tools, and Evaluation Guide

Overview

An AI agent is an application that uses a language model to interpret a goal, decide which steps are needed, use approved tools or information sources, and return an outcome. Some agents complete a fixed sequence of actions; others can choose between tools or repeat steps until a stopping condition is reached.

The right design depends on the task. A support assistant that classifies requests and drafts replies may need a simple workflow. An operations agent that checks records, calls several APIs, and requests approval for a sensitive action needs stronger controls. Before choosing a framework or model, define the job in operational terms:

• Input: What information does the agent receive, and what formats are allowed?
• Outcome: What counts as a successful result?
• Actions: Which tools can the agent call, and which actions require human approval?
• Boundaries: What must the agent refuse, escalate, or leave unchanged?
• Evidence: Which facts, records, or sources must support its response?
• Measurement: How will you detect incorrect, incomplete, slow, or unnecessarily expensive runs?

A dependable starting architecture is often a single model inside a normal application workflow. Add tool calling, memory, RAG, or multiple agents only when a specific requirement justifies the added complexity. For a broader comparison of design options, see AI agent architecture patterns.

Checklist by scenario

Scenario 1: A conversational assistant with no external actions

Use this pattern when the agent answers questions, explains procedures, or drafts content without changing external systems.

• Write a system prompt that defines the role, audience, tone, scope, and refusal or escalation behavior.
• Separate stable instructions from the user’s request and from any retrieved context.
• Specify the response format. Use headings, fields, bullets, or a schema when downstream processing depends on consistency.
• Include a clear uncertainty rule, such as asking a clarifying question when required information is missing.
• Create test cases for normal requests, ambiguous requests, conflicting instructions, and unsupported topics.

Prompt engineering is most useful here when it turns vague expectations into observable rules. A good prompt does not need to describe every possible conversation; it should make the important decisions explicit.

Scenario 2: An agent that uses tools or APIs

Use this pattern when the agent must search, calculate, retrieve records, create tickets, send messages, or perform another external action.

• Give every tool a narrow purpose and a precise input schema.
• Describe required parameters, permitted values, failure responses, and whether the operation is read-only or mutating.
• Validate model-generated arguments in application code before execution.
• Apply authentication, authorization, rate limits, timeouts, retries, and idempotency outside the prompt.
• Require confirmation for actions that are costly, irreversible, confidential, or externally visible.
• Return useful tool errors to the orchestration layer without exposing secrets or unnecessary internal details.

Tool calling should be treated as an interface contract, not as a promise that the model will always select the correct function. Compare structured tool use with other output methods in Function Calling vs. JSON Prompting.

Scenario 3: An agent grounded in internal knowledge

Use RAG when the agent needs information that is specific to your organization, changes over time, or is too large to place directly in a prompt.

• Identify authoritative documents and define ownership for each source.
• Extract, clean, split, and index content while preserving titles, dates, permissions, and source identifiers.
• Filter retrieval by user authorization before context reaches the model.
• Instruct the agent to distinguish retrieved evidence from general reasoning.
• Require citations, document references, or an explicit “not found” response where appropriate.
• Test retrieval separately from generation so you can tell whether a failure came from missing context or a poor answer.

A RAG system is only as useful as its retrieval and access controls. The practical design principles in grounding responses with internal knowledge bases can help structure this stage.

Scenario 4: A multi-step workflow

Use an orchestrated workflow when the process has known stages, dependencies, approvals, or fallback paths. Prefer explicit application code for deterministic steps. Let the model handle interpretation, classification, drafting, or selection where language understanding is needed.

• Map each stage, input, output, owner, timeout, and failure path.
• Define a stopping condition before allowing loops or repeated tool calls.
• Persist state in a structured form rather than relying on an increasingly long conversation.
• Make retries safe and record whether an action has already completed.
• Provide a human handoff path that includes the relevant context and attempted actions.

For support, sales, and operations examples, review these AI workflow automation ideas and adapt only the stages that fit your process.

What to double-check

Architecture and model selection

Choose the simplest architecture that meets the reliability requirement. A single-agent design is easier to test and observe than a multi-agent system. Add specialized agents when separation of responsibilities produces a measurable benefit, not merely because the pattern is available.

Evaluate models against your own representative tasks. Consider instruction following, structured output, tool selection, context handling, latency, availability, and operating cost. A model comparison can inform the shortlist, but a local evaluation set should determine the final choice. See API feature and tradeoff considerations across major model providers for a framework-neutral starting point.

Prompts and context

Keep the system prompt versioned, reviewable, and separate from application secrets. State priorities when instructions may conflict. Include a few representative examples only when they clarify a difficult pattern; examples should demonstrate both valid behavior and important edge cases.

Check whether retrieved context is relevant, current, correctly permissioned, and small enough to leave room for the response. More context is not automatically better. Remove duplicated or low-value material before it reaches the model.

Evaluation and observability

Build a small evaluation set before launch. Include successful cases, boundary cases, adversarial instructions, malformed inputs, tool failures, and requests that should be declined. Score the dimensions that matter for the use case: factuality, task completion, schema validity, citation quality, safety behavior, latency, and cost.

Log the workflow trace rather than only the final answer. Useful fields can include request type, prompt version, model identifier, retrieved sources, tool name, validated arguments, errors, latency, token usage where available, approval events, and user feedback. Avoid logging secrets or sensitive data unnecessarily. The guide to AI agent observability provides a practical way to organize logs, traces, and feedback loops.

Cost and performance

Measure the full workflow, not just the model call. Repeated retrieval, oversized context, unnecessary loops, and verbose tool responses can dominate resource use. Consider caching stable results, routing simple tasks to a smaller model, batching independent work, and limiting maximum steps. Recheck these choices after traffic patterns change; the cost guidance in LLM cost optimization strategies can serve as a review list.

Common mistakes

• Starting with a framework: A framework can accelerate implementation, but it cannot define a sound workflow. Write the requirements and failure paths first.
• Making the prompt responsible for security: Prompts guide behavior; they do not replace access control, input validation, secret management, or approval gates.
• Giving tools excessive permissions: Use narrowly scoped credentials and separate read operations from write operations whenever possible.
• Allowing uncontrolled autonomy: Set step limits, timeouts, budgets, and escalation conditions. Every loop needs a reason to stop.
• Testing only ideal inputs: Include incomplete, contradictory, outdated, adversarial, and unusually long requests.
• Changing prompts without traceability: Record prompt versions and connect evaluation results to each change. See prompt versioning and change tracking for a maintainable process.
• Confusing confidence with correctness: A fluent answer or a confident tone is not evidence. Require source checks, validations, or human review where the consequence of error is material.

If the agent performs classification as one part of its workflow, add explicit confidence checks and escalation rules rather than treating every classification as final. The related guide on reliable AI classifiers covers that narrower pattern.

When to revisit

Review the agent before each major planning or release cycle, and whenever its workflow, tools, knowledge sources, model, or user population changes. A reusable maintenance checklist should include:

1. Run the evaluation set against the current production configuration.
2. Review failed traces, escalations, tool errors, and user corrections.
3. Check that permissions, source documents, schemas, and API contracts are still valid.
4. Compare latency, completion rate, invalid outputs, and resource use with the previous review.
5. Remove obsolete instructions, tools, examples, and retrieved sources.
6. Test rollback for the prompt, model, workflow, and retrieval configuration.
7. Document the change, its expected effect, and the evidence used to approve it.

Before acting, use this final implementation checklist: define the outcome; choose the simplest architecture; write and version the system prompt; constrain tools and permissions; design memory and RAG deliberately; add validation and approval gates; instrument the full trace; create representative evaluations; and establish a review date. That process keeps AI agent development grounded in observable behavior rather than novelty, and gives your team a practical way to improve the system as requirements and tools change.