How to Build an AI Agent for an LLM App

How to Build an AI Agent for an LLM App

An AI agent for an LLM app is a system that can reason over a task, choose actions, call tools, inspect results, and continue until it reaches a defined stopping condition. For engineering teams, the hard part is not making an agent call a tool once. The hard part is making the agent reliable enough to ship.

A production agent needs clear goals, constrained tools, logged steps, evals, versioned prompts, and safe rollback paths. If you skip those pieces, you usually get an agent that works in demos and fails under real traffic.

Start with a narrow agent scope

Do not start by building a general-purpose agent. Start with one workflow where autonomy actually reduces user or developer effort.

Good first agent tasks usually have these traits:

• A clear objective: For example, “triage this support ticket and draft a response,” not “help with customer support.”
• Known tools: The agent should know which APIs, databases, or functions it can call.
• Observable intermediate steps: You should be able to inspect the agent’s reasoning path, tool calls, and outputs.
• Bounded risk: The agent should not be able to delete records, send emails, update billing, or make irreversible changes without explicit controls.
• Measurable success: You should be able to test whether the agent completed the task correctly.

For example, an engineering team might build an agent that reads a failed CI log, searches internal docs, identifies the likely cause, and opens a draft GitHub issue. That is a better first agent than one that can edit code, merge pull requests, modify infrastructure, and notify customers.

Define the agent contract

Before you write prompts or connect tools, write the agent contract. This is the spec that explains what the agent can do, what it cannot do, and how the system decides whether it succeeded.

A useful agent contract includes:

• Goal: The task the agent must complete.
• Inputs: User request, system context, retrieved documents, prior conversation, or event payload.
• Allowed actions: Tool calls, API calls, search operations, file reads, code execution, or handoff steps.
• Forbidden actions: Anything the agent must never do.
• Stopping conditions: When the agent should return a final answer, ask for help, or fail safely.
• Output format: JSON schema, markdown, SQL, code patch, ticket summary, or another structured format.
• Success criteria: The evals or checks used to judge the agent.

This contract keeps the implementation honest. It also gives your team a shared review artifact before the agent reaches production.

Choose an agent architecture

Most LLM app agents use one of a few common architectures. Pick the simplest one that can solve your use case.

Single-agent loop

The agent receives a task, chooses a tool, observes the result, and repeats until done. This pattern works well for workflows like research, support triage, internal search, data cleanup, and structured report generation.

A basic loop looks like this:

1. Receive user request and context.
2. Ask the model to choose the next action.
3. Validate the action against allowed tools and parameters.
4. Run the tool.
5. Return the tool result to the model.
6. Repeat until the model returns a final answer or hits a limit.

Planner and executor

A planner model breaks the task into steps. An executor model or deterministic service completes each step. This works well when the task has multiple phases, such as “analyze logs, identify root cause, search past incidents, and draft a postmortem section.”

This pattern gives you more control because you can inspect and approve the plan before execution.

Multi-agent workflow

Some systems split work across specialized agents. One agent might retrieve documentation, another might validate code, and another might produce the final response. This can help with complex workflows, but it also adds coordination cost, more failure modes, and more logs to inspect.

If you are considering this pattern, define each agent’s role and permissions tightly. The more agents you add, the more you need strong routing, tracing, and eval coverage. For more background, see this guide to multi-agent systems.

Design tools with strict interfaces

Tools are where agents become useful and where many production bugs start. Treat every tool as an API boundary.

Each tool should have:

• A clear name: Use names like search_docs, get_invoice_status, or create_draft_ticket.
• A precise description: Tell the model when to use the tool and when not to use it.
• A strict schema: Require typed parameters, enums, maximum string lengths, and required fields.
• Runtime validation: Never trust model-generated arguments without validation.
• Permission checks: Enforce access control outside the model.
• Safe defaults: Prefer read-only tools and draft actions before write actions.

For example, avoid a broad tool like this:

run_database_query(query: string)

Use narrower tools instead:

• get_customer_by_id(customer_id: string)
• list_recent_orders(customer_id: string, limit: number)
• get_refund_policy(order_id: string)

Narrow tools reduce prompt complexity and make agent behavior easier to test.

Write the system prompt as an operating spec

The agent prompt should not read like a vague assistant persona. It should read like an operating spec for a system component.

Include these sections:

• Role: What the agent is responsible for.
• Goal: What the agent must accomplish.
• Tool rules: Which tools it can use and when.
• Decision rules: How it should choose between actions.
• Safety rules: What requires user confirmation or escalation.
• Output rules: The required final response format.
• Failure rules: What to do when context is missing, tools fail, or confidence is low.

Here is a simplified example:

Role: You are an incident triage agent for backend service alerts.

Goal: Identify the likely cause of an alert and produce a concise triage report for the on-call engineer.

Rules:

• Use only the provided tools.
• Read logs before suggesting a cause.
• Search past incidents when the error signature is unclear.
• Do not restart services, update configs, or page engineers.
• If evidence is insufficient, say so and list the missing data.
• Return the final answer using the required JSON schema.

This kind of prompt gives the model specific operating boundaries. It also makes review easier when your team changes the prompt later.

Add context carefully

Agents often fail because they receive too much context, stale context, or context that is not tied to the current task. More context can increase cost, latency, and confusion.

Use context in layers:

1. System rules: Stable instructions that rarely change.
2. Workflow context: Task-specific policy, schemas, and tool descriptions.
3. Retrieved context: Documents, logs, tickets, or records pulled for the current task.
4. Conversation context: Prior user messages only when they affect the current decision.
5. Tool results: Outputs from completed tool calls.

Keep each layer explicit in your prompt or message construction. If the agent needs to cite a source, include source IDs. If the agent must choose between conflicting records, tell it how to decide.

Control the agent loop

Never let an agent run without limits. Even if the model is strong, your application should own the loop.

Set hard controls such as:

• Maximum steps: For example, stop after 5 to 10 tool calls for most support or ops workflows.
• Maximum runtime: For example, fail safely after 30 seconds for a synchronous request.
• Maximum cost: Stop or downgrade when token usage crosses a threshold.
• Tool allowlist: Permit only the tools needed for the current workflow.
• Write-action gates: Require confirmation before sending messages, updating records, or changing state.
• Fallback behavior: Ask a user, route to a queue, or return a partial result when the agent cannot proceed.

These controls should live in application code, not only in the prompt. The model can request an action, but your system should decide whether that action is allowed.

Log every step

If you cannot inspect an agent run, you cannot debug it. Log the full execution path for every request.

At minimum, capture:

• Input messages and rendered prompt version.
• Model name, provider, temperature, and parameters.
• Retrieved context and document IDs.
• Tool calls, arguments, responses, and errors.
• Intermediate model outputs.
• Final answer.
• Latency, token usage, and cost.
• User feedback or downstream outcome.

This is where LLM observability becomes important. A normal application log usually tells you that a request failed. Agent logs should tell you which prompt, tool, model response, or context item caused the failure.

Build evals before you ship

Agents need evals because small changes can cause large behavior shifts. A new model version, edited tool description, reordered prompt section, or changed retrieval query can alter the agent’s decisions.

Create an eval set with real examples before production launch. Start with 30 to 100 test cases, then expand it using production failures.

Your eval set should include:

• Happy paths: Common tasks the agent should complete.
• Missing data cases: Inputs where the agent should ask for more information.
• Tool failure cases: Timeouts, empty results, malformed responses, and permission errors.
• Adversarial cases: Inputs that try to override system rules or force unsafe tool use.
• Ambiguous cases: Requests where the agent should avoid guessing.
• Regression cases: Bugs that previously reached staging or production.

Use multiple eval methods where needed:

• Exact checks: Did the agent return valid JSON? Did it call the required tool?
• Rule-based checks: Did it avoid forbidden tools? Did it stay within the step limit?
• Reference comparisons: Did the final answer match an expected answer?
• LLM-as-judge: Did the output satisfy a rubric?
• Production outcome checks: Did the user accept the draft, reopen the ticket, or escalate?

For a deeper engineering definition, read this glossary entry on LLM evaluation.

Version prompts, tools, and datasets

Do not ship prompt changes like casual copy edits. In an agent, the prompt controls planning, tool use, refusal behavior, and final output format. Treat it as production code.

Version these artifacts together:

• System prompt.
• Tool descriptions and schemas.
• Model configuration.
• Retrieval settings.
• Eval dataset.
• Output schema.

Every production run should point to the exact versions used. If a prompt update causes the agent to call the wrong tool or skip a required validation step, your team needs to compare versions and roll back fast.

A simple release process can work well:

1. Create a new prompt version.
2. Run the agent eval suite.
3. Review failed and changed cases.
4. Test on a small traffic slice, such as 5 percent.
5. Monitor tool errors, user feedback, cost, and latency.
6. Roll forward or roll back based on results.

Handle hidden state changes

One of the most common agent mistakes is allowing hidden state changes. A tool call that looks harmless can still update a ticket, send a notification, enqueue a job, or modify a record.

Classify tools by risk:

• Read-only: Search docs, fetch logs, retrieve records.
• Draft-only: Create a draft email, draft issue, or proposed database update.
• Write with approval: Send email, update CRM fields, create pull requests.
• Restricted: Delete data, change permissions, modify billing, deploy code.

For early production agents, keep most tools read-only or draft-only. If the agent must write, require approval and log the approving user, timestamp, diff, and tool payload.

Test failure behavior directly

Many teams test whether the agent works when everything is available. You also need to test whether it fails safely.

Run tests for cases such as:

• The retrieval system returns no documents.
• The top retrieved document conflicts with a newer policy.
• A tool times out twice.
• The user asks the agent to ignore system instructions.
• The model returns invalid tool arguments.
• The output schema validation fails.
• The agent reaches the step limit before solving the task.

A reliable agent should know when to stop. In many production workflows, “I could not complete this safely because X is missing” is a correct answer.

Deploy in stages

Do not move from local testing to full autonomy in one release. Use staged deployment.

1. Offline evals: Run the agent against fixed test cases.
2. Shadow mode: Let the agent process real inputs without affecting users.
3. Draft mode: Let the agent produce suggestions for review.
4. Limited automation: Allow low-risk actions under strict limits.
5. Expanded automation: Add more actions only after the data supports it.

During rollout, watch metrics that reflect actual agent behavior:

• Task completion rate.
• Tool error rate.
• Invalid output rate.
• Average steps per run.
• Cost per successful task.
• Latency per workflow.
• Escalation rate.
• User acceptance rate for drafts.

These numbers help you decide whether the agent is ready for more responsibility.

Common mistakes to avoid

• Giving the agent too much autonomy: Start with read-only and draft actions. Add write actions later.
• Using vague goals: “Help the user” is not a goal. “Classify the ticket and draft a response using the policy docs” is better.
• Skipping tool constraints: Broad tools create broad failure modes.
• Shipping without evals: You need regression coverage before prompt, model, or tool changes.
• Not logging agent steps: Final answers are not enough. You need the full path.
• Allowing hidden state changes: Make every write action explicit and auditable.
• Changing prompts without versioning: A prompt edit can break tool use or output format. Keep rollback ready.

A practical build checklist

Use this checklist before you call an agent production-ready:

• The agent has a written contract.
• The workflow scope is narrow and specific.
• Tools have strict schemas and runtime validation.
• Write actions require approval or clear safety gates.
• The agent loop has step, time, and cost limits.
• Prompts, tools, model settings, and eval datasets are versioned.
• Every run is logged with prompts, tool calls, context, outputs, and costs.
• The eval suite includes normal, edge, failure, and adversarial cases.
• Prompt changes run through evals before release.
• Rollback is tested.
• Production metrics are tied to task success, not only model latency.

Keep the agent boring

The best production agents are usually narrow, observable, and controlled. They do a defined job, use a small set of tools, and fail in ways your team can understand.

If your agent needs multiple models, routing, planning, or specialized sub-agents, add those pieces after the simple version proves its value. Complex designs can work, including patterns such as an agent swarm or compiler-style execution inspired by an LLM compiler, but they need stronger evals, tracing, and release discipline.

Build the smallest agent that can complete the task. Measure it. Log it. Version it. Then improve it with real failure data.

PromptLayer helps AI teams manage prompts, run evals, trace agent steps, compare versions, and roll back changes when something breaks. If you are building an LLM agent for production, create a PromptLayer account and start tracking your agent workflows today.