How to Define AI Agents for LLM Apps

What counts as an AI agent in an LLM app?

An AI agent is an LLM-powered system that can choose actions, use tools, inspect results, and continue working toward a defined goal within explicit boundaries.

That definition has a few important parts:

• Goal: The agent has a task to complete, such as resolving a support ticket, scheduling a meeting, or generating and testing a code change.
• Decision loop: The model can decide what to do next based on prior context and tool results.
• Tools: The agent can call external functions, APIs, databases, browsers, file systems, or code execution environments.
• State: The agent keeps track of progress, intermediate results, constraints, and errors.
• Boundaries: The agent has limits around permissions, cost, time, actions, and escalation rules.

If your app sends one prompt to a model and returns the answer, it is not an agent. If your app runs a fixed three-step chain with no model-selected actions, it is usually a workflow, not an agent. If your app lets the model decide whether to search docs, call a CRM API, ask a follow-up question, or stop, you are closer to an agent.

A practical definition for engineering teams

Use this definition in design docs and architecture reviews:

An AI agent is an LLM-controlled execution loop where the model can choose among allowed actions, call tools, observe results, and decide whether to continue, stop, or escalate.

This definition separates agents from generic LLM features. It also gives your team something testable. You can inspect the allowed actions, trace the loop, check stop conditions, and evaluate success across realistic tasks.

Agents are not defined by complexity

Many teams confuse autonomy with complexity. A 12-step prompt chain can still be a deterministic workflow. A small assistant with one tool can be an agent if the model decides when to call that tool and how to respond to its result.

For example, this is a simple LLM workflow:

User message
  -> classify intent
  -> retrieve matching help article
  -> draft response
  -> return response

The model does not choose the process. Your application controls every step.

This is a bounded AI agent:

User message
  -> agent receives goal and policy
  -> model chooses next action:
       1. ask clarification
       2. search docs
       3. check account status
       4. create support ticket
       5. stop and answer
  -> tool result is added to state
  -> model chooses next action
  -> stop when done, blocked, or over budget

The second system has a model-directed control loop. It also has a limited action space, which makes it easier to test and safer to ship.

The five components of an LLM agent

1. Goal

The goal tells the agent what outcome to work toward. Keep it concrete.

Weak goal:

Help the user with billing.

Better goal:

Resolve the user's billing question by checking account status, explaining invoice line items, and creating a support ticket if account changes are required.

A good goal gives the agent enough direction to choose actions without inventing a broad mission.

2. Policy

The policy defines what the agent can and cannot do. This belongs in system instructions, tool schemas, application code, and eval cases.

Example policy for a billing agent:

• The agent may read invoices, subscription status, and payment history.
• The agent may not issue refunds without approval.
• The agent may not change billing email addresses.
• The agent must ask for confirmation before creating a support ticket.
• The agent must stop after 8 tool calls or 90 seconds.
• The agent must escalate if the user asks about legal, tax, or compliance terms.

Do not rely on a prompt alone for sensitive controls. Enforce permissions in code. If a tool should only allow read access, the API key and backend endpoint should only allow read access.

3. Tools

Tools are the actions an agent can take. Common tools include search, retrieval, SQL queries, ticket creation, email drafting, browser automation, code execution, and internal API calls.

Good tool definitions include:

• A narrow name, such as get_invoice_by_id instead of billing_tool.
• A clear description of when to use the tool.
• A strict input schema.
• Typed outputs that the agent can interpret reliably.
• Permission checks outside the model.
• Timeouts and retry limits.

Example tool schema:

{
  "name": "get_invoice_by_id",
  "description": "Fetch invoice metadata and line items for a customer invoice. Read-only.",
  "input_schema": {
    "type": "object",
    "properties": {
      "customer_id": { "type": "string" },
      "invoice_id": { "type": "string" }
    },
    "required": ["customer_id", "invoice_id"]
  }
}

If the tool name, schema, or description is vague, the agent will make worse decisions. Treat tool design as API design.

4. State

State is the working memory of the agent. It can include the original user request, previous messages, tool calls, retrieved documents, intermediate plans, failed attempts, and stop reasons.

Keep state structured where possible. A long transcript is easy to implement, but it becomes hard to debug and expensive to run. Store key fields separately:

• goal: Resolve invoice question
• user_id: cus_123
• known_invoice_id: inv_456
• tools_used: get_invoice_by_id, search_billing_docs
• open_questions: user has not confirmed ticket creation
• stop_reason: waiting_for_user_confirmation

This makes traces easier to read and evals easier to write.

5. Control loop

The control loop decides how the agent runs. A simple loop looks like this:

while not stopped:
  render prompt with goal, state, policy, and tool list
  call model
  if model returns tool call:
    validate permissions and schema
    execute tool
    append result to state
  if model returns final answer:
    stop
  if budget, time, or safety limit is reached:
    stop or escalate

The loop is where production reliability lives. You need budget limits, schema validation, retries, idempotency keys, logging, and clear stop conditions.

Common agent patterns

Static agents

A static agent has a fixed role, fixed tool set, and predictable loop. This pattern works well for production use cases such as customer support triage, internal search, invoice explanation, and CRM updates.

Static agents are easier to evaluate because the action space does not change on each request. If you are starting a new agent project, this is usually the best first version. You can read more about static agents if you need a tighter taxonomy for your architecture doc.

Dynamic agents

A dynamic agent can adjust its role, tool set, plan, or sub-agents based on the task. This can help with broad tasks, but it increases the testing surface quickly.

Use dynamic behavior only when the task requires it. For example, a code repair agent may need to inspect a repo, choose a test command, edit files, and rerun tests. A billing FAQ assistant probably does not need dynamic tool selection beyond a small list of read-only APIs.

If your system changes its behavior at runtime, define what is allowed to change and what remains fixed. The PromptLayer glossary entry on dynamic agents is useful when you need to describe this pattern precisely.

Plan-and-execute agents

A plan-and-execute agent first creates a plan, then executes steps against that plan. This pattern works when the task has multiple dependencies, such as researching a company, drafting an outbound email, checking CRM state, and logging the result.

The main risk is stale planning. If tool results contradict the original plan, the agent needs permission to revise it. You also need evals that check whether the plan was followed, whether changes were justified, and whether the final answer matches the evidence collected.

For teams formalizing this pattern, plan-and-execute agents give you a clear starting point.

Compiler-style agents

Some systems translate a high-level task into an executable graph or intermediate representation before running it. This is useful when you need repeatability, static checks, or parallel execution.

For example, a data analysis agent might turn a user question into a query plan, validate table access, run SQL, generate a chart, and return a written answer. If you are exploring this style, the concept of an LLM compiler may fit your design.

Before and after: upgrading a workflow into a bounded agent

Before: deterministic support workflow

Input: "Why was I charged twice?"

Step 1: classify_intent(user_message)
Step 2: retrieve_docs(intent)
Step 3: generate_answer(user_message, docs)
Output: response

This workflow is simple and reliable. It may be enough if most billing questions can be answered with documentation. It breaks down when the assistant needs account-specific data or must decide whether the issue requires a ticket.

After: bounded billing agent

Goal:
  Resolve billing questions using read-only account tools and approved help docs.

Allowed tools:
  - search_billing_docs(query)
  - get_subscription_status(customer_id)
  - get_invoice_by_id(customer_id, invoice_id)
  - create_support_ticket(customer_id, summary)

Rules:
  - Read-only account access, except ticket creation.
  - Ask for confirmation before creating a ticket.
  - Never issue credits, refunds, or plan changes.
  - Stop after 6 tool calls.
  - Escalate if payment fraud is suspected.

Loop:
  1. Inspect user request.
  2. Choose a tool or ask a question.
  3. Observe result.
  4. Continue until answered, blocked, or escalated.

This upgraded system is an agent because the model chooses the next action. It remains bounded because the tools, permissions, and stop rules are explicit.

What a useful tool-call trace looks like

If you cannot trace an agent run, you cannot debug it. Every production agent should record the prompt version, model call, tool request, tool result, latency, token usage, error state, and final output.

A readable trace might look like this:

Run ID: run_789
User: "Why was I charged twice this month?"

Step 1: model_decision
  action: get_subscription_status
  input: { "customer_id": "cus_123" }
  reason: "Need current subscription and billing cycle before answering."

Step 2: tool_result
  tool: get_subscription_status
  output: {
    "plan": "Pro",
    "billing_cycle": "monthly",
    "status": "active"
  }

Step 3: model_decision
  action: get_invoice_by_id
  input: { "customer_id": "cus_123", "invoice_id": "latest" }
  reason: "Need invoice line items to compare charges."

Step 4: tool_result
  tool: get_invoice_by_id
  output: {
    "invoice_id": "inv_456",
    "line_items": [
      { "description": "Pro monthly plan", "amount": 49 },
      { "description": "Additional seat prorated charge", "amount": 18 }
    ]
  }

Step 5: final_answer
  answer: "You were not charged twice for the same plan. The second charge is a prorated seat charge..."
  stop_reason: "resolved"

The trace should make the run explainable to an engineer, not just to the model. You should be able to answer these questions in under 2 minutes:

• Which prompt version ran?
• Which tools were available?
• Which tools were called?
• What did each tool return?
• Did the agent follow policy?
• Where did latency and cost accumulate?
• What stop condition ended the run?

Define permissions before you ship

Agent permissions should be boring and explicit. Write them down before implementation.

Prompt instructions help, but enforcement should live in your application layer. The model can request an action. Your system decides whether that action is allowed.

Failure handling is part of the agent definition

An agent definition is incomplete if it only describes the happy path. Production agents need clear behavior for common failures.

• Tool timeout: Retry once, then tell the user the system cannot access the data right now.
• Invalid tool arguments: Ask the model to repair the call once, then stop if invalid again.
• Permission denied: Do not retry. Explain the limitation or escalate.
• Conflicting tool results: ask a clarifying question or route to review.
• Low confidence: stop and ask for more information instead of guessing.
• Budget exceeded: return a partial answer with a clear limitation, or escalate.

Give each failure type a named stop reason. This makes analytics and evals much easier.

stop_reason:
  - resolved
  - waiting_for_user
  - permission_denied
  - tool_timeout
  - max_tool_calls_reached
  - policy_escalation
  - low_confidence

How to evaluate an agent

You need evals at three levels: final output, tool behavior, and policy compliance.

Final output evals

These check whether the agent answered the user correctly.

• Did the answer use the right source data?
• Did it avoid unsupported claims?
• Did it match the expected format?
• Did it resolve the user’s task?

Tool-use evals

These check whether the agent took the right actions.

• Did it call the correct tool?
• Did it avoid unnecessary tool calls?
• Did it pass valid arguments?
• Did it stop after enough evidence?

Policy evals

These check whether the agent respected boundaries.

• Did it avoid restricted actions?
• Did it ask for confirmation before write operations?
• Did it escalate when required?
• Did it stop when limits were reached?

For agent evals, include adversarial and messy cases. Real users provide partial IDs, vague requests, duplicate tickets, stale account states, and conflicting instructions. Your eval set should include those cases before the agent reaches production traffic.

Agent architecture for LLM apps

A production agent architecture usually has these layers:

Client
  -> Agent API
      -> Auth and permission checks
      -> Prompt rendering
      -> Model call
      -> Tool router
          -> Tool schema validation
          -> Backend services
          -> External APIs
      -> State store
      -> Trace and evaluation store
      -> Final response

Do not hide tool execution inside unlogged helper functions. Keep the tool router observable. Each tool call should produce a structured event with inputs, outputs, latency, and errors.

If you are using OpenAI’s agent tooling, connect agent runs to your tracing and eval workflow early. PromptLayer’s OpenAI Agents SDK integration can help your team track prompts, tool calls, and agent behavior in one place.

A checklist for defining your agent

Before you build, answer these questions in a short design doc:

1. Goal: What user task should the agent complete?
2. Non-goals: What should it never attempt?
3. Users: Who can access it?
4. Tools: Which tools can it call?
5. Permissions: What can each tool read or write?
6. State: What data persists during and after a run?
7. Loop: How does it decide, act, observe, and stop?
8. Limits: What are the max tool calls, tokens, time, and cost?
9. Escalation: When does it route to a person or another system?
10. Traces: What events are logged for each run?
11. Evals: What test cases must pass before launch?
12. Rollback: How do you disable or downgrade the agent if it fails?

If you cannot answer these questions, the system is not ready for production agent behavior.

Common mistakes to avoid

• Calling every LLM interaction an agent: A single completion, RAG response, or fixed prompt chain is not automatically an agent.
• Shipping tools without permission checks: Tool access should be enforced by backend code, not trusted to the model.
• Skipping traces: You need run-level visibility into prompts, decisions, tool calls, and outputs.
• Testing only final answers: Agents can produce a correct answer through an unsafe or expensive path. Test the path too.
• Giving the model broad tools: Prefer narrow tools with typed schemas and limited permissions.
• Ignoring stop conditions: Every agent loop needs limits for time, cost, tool calls, and uncertainty.
• Adding autonomy where a workflow is enough: If the process is known and stable, a deterministic workflow may be cheaper and more reliable.

A good agent definition is operational

The best agent definition is one your engineering team can implement, trace, evaluate, and debug. It should describe the goal, tools, state, permissions, loop, stop conditions, and failure behavior.

Start narrow. Give the agent a small action space. Add tools only when evals show the current version cannot complete important tasks. Keep the trace readable. Treat prompts, tool schemas, and eval datasets as versioned production assets.

That approach will help you ship LLM agents that behave predictably under real user traffic, not only in demos.

PromptLayer helps AI teams manage prompts, trace tool calls, evaluate agent behavior, and improve LLM applications over time. To start tracking and testing your agents, create an account at https://dashboard.promptlayer.com/create-account.