How to Write an Agentic Definition for Your Team

How to Write an Agentic Definition for Your Team

An agentic definition gives your team a shared standard for when an LLM-powered system should be called an agent, how it should behave, and how you will measure it in production.

This matters for engineering teams because “agentic” often gets used too loosely. A chatbot with a tool call is not automatically an agent. A prompt chain is not automatically an agent. A workflow with hidden orchestration logic might act agentic even if nobody documented it that way.

If your team does not define the term clearly, you will run into practical problems:

• Product, engineering, and leadership will use the same word for different systems.
• Developers will ship unclear control flow because the boundary between model decision-making and application logic is vague.
• Reviewers will miss required approval steps before high-risk actions.
• Evaluation coverage will focus on final answers instead of intermediate decisions, tool calls, and stopping conditions.
• Observability will show requests and responses, but not the reasoning path your system used to complete a task.

A useful agentic definition should be specific enough to guide architecture, evals, tracing, release reviews, and incident debugging.

Start With a Working Definition

Use a definition that describes system behavior, not marketing language.

An agentic system is an LLM-powered system that pursues a goal through a feedback loop: it chooses actions, uses tools or external context, observes results, updates state, and continues until it reaches a stopping condition or asks for approval.

This definition gives you five pieces to inspect:

• Goal: What outcome is the system trying to complete?
• Decision point: Where does the model decide the next action?
• Action: What tool, API, retrieval step, message, or workflow step can it trigger?
• Observation: What result does it receive after acting?
• Stopping condition: When does it stop, return control, or ask a person to approve?

If your system does not include a loop or model-selected next action, call it something else. “LLM workflow,” “prompt chain,” “AI assistant,” or “tool-using chatbot” may be more accurate.

Separate Agents, Chatbots, Workflows, and Prompt Chains

One common mistake is equating chatbots with agents. A chatbot can be agentic, but many are not.

Use a simple classification table in your team docs:

This table prevents a small naming issue from becoming an architecture issue. If the system follows a fixed sequence, say that. If the model chooses the next step, document that clearly.

Write the Definition as an Engineering Contract

Your definition should tell engineers what must exist in the system. Treat it as a lightweight contract.

A strong team definition usually includes:

• Scope: Which products, services, or internal tools the definition applies to.
• Required capabilities: Planning, tool use, memory, retrieval, state updates, or approval gates.
• Control boundaries: Which decisions the model can make and which decisions the application owns.
• Risk boundaries: Actions that require approval, rate limits, or policy checks.
• Trace requirements: What you log for each step.
• Eval requirements: What you test before release and after changes.
• Failure handling: How the system stops, retries, escalates, or asks for help.

Here is a practical template you can adapt:

Our team defines an agentic system as an LLM-powered workflow that:
1. Receives a goal or task.
2. Uses the model to choose at least one next action.
3. Can call tools, query context, or update state.
4. Observes the result of each action.
5. Uses the observation to decide whether to continue, stop, retry, or ask for approval.
6. Emits a trace of prompts, model outputs, tool calls, observations, and final status.
7. Has explicit stopping conditions and risk controls.

You can then add exclusions:

We do not call a system agentic when:
1. The application runs a fixed prompt chain with no model-selected action.
2. The model only generates text for a single user request.
3. Tool calls are fully predetermined by application code.
4. We cannot inspect intermediate steps in traces or logs.

Make Hidden Orchestration Visible

Many teams accidentally hide the most important part of an agentic system in application code. The prompt looks simple, but the service performs retries, routing, retrieval, ranking, tool selection, fallback calls, and state updates around it.

If the orchestration is hidden, your team cannot reason about the agent’s behavior. You will also struggle to evaluate changes because nobody can see which part changed: the prompt, model, tool schema, context, routing rule, or retry policy.

Document the full loop. A simple text diagram is often enough for a design review:

User goal
  ↓
Task intake prompt
  ↓
Model chooses next action
  ↓
Tool call: search_docs(query)
  ↓
Observation: top 5 documents
  ↓
Model updates task state
  ↓
Decision:
  ├─ call another tool
  ├─ ask user for missing detail
  ├─ request approval
  └─ produce final answer

Add this diagram near the definition in your internal docs. If your team uses traces, include a screenshot of a real run next to the diagram. The screenshot should show the prompt version, model, tool call arguments, tool response, next decision, and final output.

Define Approval Steps Before You Need Them

Another common mistake is ignoring approval steps until the system reaches production. Approval rules should be part of the agentic definition, not an afterthought.

Write down which actions the system can take on its own and which actions need a person to approve. Be specific.

• Allowed without approval: Retrieve documentation, classify an issue, draft a response, summarize logs, create a ticket draft.
• Requires approval: Send customer email, issue refund, modify production config, merge code, delete data, escalate account status.
• Blocked entirely: Access data outside the user’s permission scope, run destructive database operations, bypass compliance checks.

Approval does not always mean a person must approve every step. You can use policy checks, confidence thresholds, and staged permissions. For example, an incident assistant might run diagnostics automatically but need approval before restarting a service.

Your definition should answer this question: What is the most powerful action this system can take, and what must happen before it takes that action?

Include Observability in the Definition

You cannot define agentic behavior only by what the user sees. The user may only see a final answer, while the system performed eight hidden steps to get there.

Your definition should require step-level observability. At minimum, capture:

• Input goal or user request
• Prompt template and prompt version
• Model name and parameters
• Retrieved context and dataset references where appropriate
• Model-selected action
• Tool name and tool arguments
• Tool result or error
• State update after each observation
• Approval request and approval result
• Stopping condition
• Final output
• Latency, cost, and retry count

Here is a compact trace example:

run_id: agt_48291
goal: "Investigate why invoice INV-1034 failed"
prompt_version: billing_agent:v12
model: gpt-4.1
step_1:
  decision: call_tool
  tool: get_invoice
  args: {"invoice_id": "INV-1034"}
  observation: {"status": "failed", "reason": "card_declined"}
step_2:
  decision: call_tool
  tool: get_customer_payment_history
  args: {"customer_id": "cus_901"}
  observation: {"failed_attempts_30d": 3}
step_3:
  decision: request_approval
  reason: "Send customer payment update email"
  approval_status: approved
step_4:
  decision: final
  output: "Invoice failed due to card decline. Customer email sent after approval."
stopping_condition: approved_action_completed

This trace makes the definition testable. Without it, “agentic” becomes a label you cannot debug.

Define the Evaluation Standard

Agentic systems need evals that cover more than final output quality. If the system chooses actions, your evals must check those choices.

Add these eval categories to your definition:

• Goal completion: Did the system complete the requested task?
• Action selection: Did it choose the right tool or next step?
• Tool argument quality: Did it pass correct, safe, and complete arguments?
• Context use: Did it use relevant retrieved context without inventing unsupported facts?
• Approval behavior: Did it ask for approval when required?
• Stopping behavior: Did it stop at the right time instead of looping, retrying forever, or acting too early?
• Recovery: Did it handle tool errors, missing data, and ambiguous requests correctly?
• Cost and latency: Did it stay within your production limits?

For example, if you are building a support triage agent, do not only grade the final message. Grade whether it selected the right queue, retrieved the right account data, avoided restricted fields, asked for missing information, and stopped before sending a customer-facing reply without approval.

Create a Before and After Definition

If your team already uses the word “agentic,” improve the definition instead of starting with a debate. Show a weak version and a stronger replacement.

Before

Our support agent uses an LLM to answer customer questions and take actions.

This is too vague. It does not say what actions are allowed, whether the model chooses them, what gets logged, or when approval is needed.

After

Our support agent is an LLM-powered workflow that receives a customer support goal, selects the next step using the model, retrieves account and policy context, calls approved support tools, observes tool results, and continues until it resolves the ticket, asks the customer for missing information, or requests teammate approval.

The model may choose among these actions:
- search_policy_docs
- get_order_status
- classify_ticket
- draft_customer_reply
- request_teammate_approval

The model may not:
- send customer messages without approval
- issue refunds
- change account status
- access data outside the authenticated customer's account

Every run must log prompt version, model, tool calls, tool arguments, tool results, approval events, final output, cost, latency, and stopping condition.

The after version gives engineers enough detail to build, evaluate, review, and monitor the system.

Use a Definition Review Checklist

Before your team accepts an agentic definition, review it against a concrete checklist.

1. Does it identify the goal the system pursues?
2. Does it say where the model makes decisions?
3. Does it list the tools or action types the system can use?
4. Does it define what the system observes after each action?
5. Does it describe state updates or context changes?
6. Does it include stopping conditions?
7. Does it define approval requirements for risky actions?
8. Does it name actions that are blocked?
9. Does it require traces for prompts, tool calls, and observations?
10. Does it include eval criteria for action selection and stopping behavior?
11. Does it distinguish the system from a chatbot, prompt chain, or fixed workflow?
12. Could a new engineer use the definition to understand production behavior?

If the answer is no for several items, the definition is not ready.

Keep the Definition Close to the System

Do not bury the definition in a planning doc that nobody updates. Put it where engineers will see it during implementation and review.

Good locations include:

• The service README
• The prompt registry description
• The evaluation plan
• The release checklist
• The tracing dashboard notes
• The architecture decision record

Update the definition when behavior changes. If you add a new tool, approval rule, retry strategy, or model-selected action, update the definition and evals in the same pull request.

A Short Template You Can Copy

Use this version when you need a concise definition for an engineering doc:

We define [system name] as agentic because it uses an LLM to choose actions toward [goal].

The system can:
- [allowed action/tool 1]
- [allowed action/tool 2]
- [allowed action/tool 3]

The system observes:
- [tool result, retrieval result, user response, state change]

The system continues until:
- [success condition]
- [missing information condition]
- [approval condition]
- [failure condition]

The system must ask for approval before:
- [risky action 1]
- [risky action 2]

The system must never:
- [blocked action 1]
- [blocked action 2]

Each run must trace:
- prompt version
- model
- selected action
- tool arguments
- tool result
- state update
- approval event
- stopping condition
- final output
- cost and latency

We evaluate the system on:
- goal completion
- action selection
- tool argument quality
- context use
- approval behavior
- stopping behavior
- recovery from errors

Final Guidance

A good agentic definition should reduce ambiguity. It should tell your team what the model controls, what the application controls, what the system can do, what it cannot do, how it stops, and how you inspect each run.

If your definition does not affect architecture, evals, tracing, or approval rules, it is probably too vague. Tighten it until it helps someone make an engineering decision.

PromptLayer helps AI teams manage prompts, trace agentic workflows, evaluate changes, inspect tool calls, and track production behavior across LLM applications. If your team is defining or shipping agentic systems, create a PromptLayer account here: https://dashboard.promptlayer.com/create-account.