How to Define Agentic AI for Your LLM App

How to Define Agentic AI for Your LLM App

An LLM app is agentic when it can pursue a goal through a controlled loop of planning, action, observation, and adjustment.

The app does more than send one prompt to a model and return one response. It can decide what step to run next, call tools, inspect tool results, update state, and continue until it reaches a stop condition. The key point is control. Agentic behavior should be bounded by permissions, budgets, traces, evals, and clear failure paths.

This definition matters because teams often label any LLM workflow as an “agent.” A chat assistant that answers based on retrieved documents may be valuable, but it is not necessarily agentic. A support resolution system that checks account status, searches policies, drafts a reply, detects missing information, asks a follow-up question, and retries after a tool error has stronger agentic properties.

A practical definition

Use this definition when you are designing, reviewing, or documenting your LLM application:

An agentic AI system is an LLM-powered application that can choose and execute multiple steps toward a goal by using tools, reading intermediate results, updating state, and stopping based on defined criteria.

This definition keeps the focus on application behavior, not model branding. The same model can power a simple summarizer, a fixed workflow, or an agentic workflow depending on how your system is built.

What makes an LLM app agentic?

You can evaluate agentic behavior across six dimensions.

1. Goal-directed execution

The system receives a goal, not just a single instruction. The goal may come from a user, a queue, an API request, or an internal workflow.

Examples:

• Support agent: “Resolve this billing complaint or route it with a complete summary.”
• Coding agent: “Fix the failing test in this repository and open a pull request.”
• Data analysis agent: “Find the cause of last week’s conversion drop and create a short report.”
• Internal workflow assistant: “Prepare an onboarding checklist for this new hire based on role, location, and systems access.”

A goal gives the system room to decide which steps are needed. A single prompt usually does not.

2. Step selection

An agentic system can choose the next operation based on the current state. This may involve a planner prompt, a routing model, a policy engine, a task graph, or code that delegates some decision-making to an LLM.

For example, a data analysis agent might choose between querying a warehouse, inspecting a dashboard, asking the user for a metric definition, or ending with a report. A fixed workflow would run the same sequence every time.

3. Tool use

Most production agentic systems call tools. Tools can include search, retrieval, SQL execution, code execution, ticket updates, CRM actions, calendar actions, file operations, or internal APIs.

Tool use alone does not make an app agentic. A fixed RAG pipeline uses retrieval as a tool, but it may still follow one path: retrieve context, call model, return answer. Agentic behavior appears when the system decides which tool to call, when to call it, how to respond to the result, and whether another step is needed.

4. Observation and state updates

After each action, the system must read the result and update state. This state may include conversation history, scratchpad output, task status, tool responses, memory entries, retry counts, permissions, cost usage, and evaluation signals.

For a coding agent, state might include changed files, test output, compiler errors, and the current patch. For a support agent, state might include customer tier, account status, policy matches, previous replies, and escalation rules.

5. Stop conditions

An agentic loop needs explicit stopping rules. Without them, you risk long-running loops, repeated tool calls, high cost, and confusing user experiences.

Useful stop conditions include:

• The system has produced an answer that satisfies the task schema.
• A required approval is missing.
• The system hit a maximum step count, such as 8 tool calls or 3 retries.
• The confidence score or eval result is below your threshold.
• A tool returned a non-recoverable error.
• The task requires escalation to a human reviewer or another system.

6. Guardrails and permissions

Agentic systems need stronger controls because they can take actions. A summarizer can produce a bad summary. A support agent with write access can refund the wrong order, change the wrong plan, or send an incorrect email.

Define permissions by tool, user role, environment, and risk level. For example, your support agent may read billing history in production but require approval before issuing credits over $50. Your coding agent may edit files in a branch but cannot merge to main. Your data analysis agent may run read-only SQL but cannot export rows containing sensitive fields.

Agentic vs fixed workflow vs simple LLM call

Teams often need a shared vocabulary before they can make architecture decisions. Use this comparison:

Suggested diagram: Add a side-by-side visual comparing a fixed workflow and an agentic workflow. The fixed workflow should show a straight line: input, retrieval, model call, output. The agentic workflow should show a loop: goal, plan, tool call, observation, state update, decision, stop or continue.

The agent loop

A basic agent loop usually looks like this:

1. Receive goal: The user or system provides the task.
2. Load context: The app gathers instructions, user data, memory, retrieved documents, and policies.
3. Plan next step: The model or controller selects the next action.
4. Call tool: The app executes a tool call, API request, retrieval step, code command, or database query.
5. Observe result: The app records the output, error, or intermediate result.
6. Update state: The app stores progress, decisions, traces, and relevant outputs.
7. Evaluate progress: The app checks quality, safety, permissions, and completion criteria.
8. Stop or continue: The app returns an answer, asks for clarification, escalates, or runs another step.

Suggested diagram: Add a loop diagram with these nodes: goal, context, planner, tool call, observation, memory/state, guardrails, eval, final response. Show traces running across every step.

What agentic AI is not

Use these exclusions to keep your architecture discussions precise.

• A chatbot is not automatically agentic. Many chatbots answer questions without selecting actions or updating task state.
• Tool calling is not automatically agentic. A model that always calls the same retrieval tool before answering may still be a fixed pipeline.
• Multiple prompts are not automatically agentic. A chain that always runs prompt A, then prompt B, then prompt C is a deterministic workflow.
• Autonomy is not unlimited access. Production systems should restrict actions, budgets, data access, and execution environments.
• Agentic behavior does not remove the need for evaluation. More steps usually create more failure modes.

Examples by application type

LLM-powered support agent

A non-agentic support assistant might retrieve policy documents and draft a reply. An agentic support system can inspect ticket history, check order status, decide whether a refund policy applies, ask for missing information, draft a response, and route the case if it exceeds policy limits.

Production controls should include:

• Read and write permissions for CRM and billing tools.
• Approval thresholds for credits, refunds, cancellations, or plan changes.
• Trace logs for every tool call and generated message.
• Regression tests for policy compliance.
• Escalation rules for sensitive cases.

Coding agent

A coding assistant that answers questions about a codebase is usually not agentic. A coding agent becomes agentic when it can inspect files, edit code, run tests, read failures, revise its patch, and stop when checks pass or a limit is reached.

Useful constraints include:

• Run only in a sandbox or isolated branch.
• Limit file write access to the target repository.
• Require tests before proposing a pull request.
• Block access to secrets and production credentials.
• Record diffs, commands, model calls, and test output in traces.

Data analysis agent

A data analysis agent can translate a business question into queries, inspect results, run follow-up queries, detect missing definitions, and produce a report with caveats. This is more complex than a single text-to-SQL call because the system may need several rounds of query, review, and correction.

Controls should include:

• Read-only database access.
• Query timeouts and row limits.
• Approved metric definitions.
• PII filtering before output.
• Checks for unsupported causal claims.

Internal workflow assistant

An internal workflow assistant can coordinate steps across HR, IT, finance, or operations systems. For example, it might prepare onboarding tasks by checking the employee’s department, location, manager, device needs, and system access requirements.

This can be agentic if the assistant chooses which systems to query, identifies missing approvals, updates a checklist, and routes exceptions. It should not receive broad write access by default. Start with read-only tasks, then add limited write operations after you have strong traces and evals.

Architecture for an agentic LLM app

A production agentic system usually includes these components:

• User or system goal: The task request, ticket, queue item, or workflow trigger.
• Orchestrator: The service that manages state, calls the model, executes tools, and enforces limits.
• Model: The LLM used for planning, routing, generation, extraction, or critique.
• Prompts: Versioned instructions for planning, tool selection, output formatting, refusal behavior, and task completion.
• Tools: APIs, search systems, databases, code execution, file access, SaaS actions, and internal services.
• Memory and state: Conversation state, task progress, retrieved context, user preferences, intermediate outputs, and prior decisions.
• Guardrails: Permissions, validation, schemas, policy checks, rate limits, budget limits, and approval gates.
• Traces: Step-by-step records of prompts, model outputs, tool calls, errors, latency, cost, and final results.
• Evals: Tests that measure task success, policy compliance, tool accuracy, output quality, and regression risk.

Suggested diagram: Add an architecture diagram showing user goal, orchestrator, model, prompt versions, tools, memory, traces, guardrails, and evals. Show traces collecting data across model calls and tool calls, then feeding datasets and evaluation runs.

How to decide whether your app should be agentic

Agentic architecture adds complexity. Use it when the task needs adaptive step selection. Avoid it when a fixed workflow is easier to test and maintain.

Good candidates for agentic design include tasks where:

• The required steps vary by input.
• The app must inspect intermediate results before choosing the next action.
• Several tools may be needed, but not always in the same order.
• The system must recover from partial failures, missing data, or ambiguous requests.
• A fixed workflow would create many brittle branches.

Poor candidates include tasks where:

• The workflow is already stable and deterministic.
• The cost of a wrong action is high and approval cannot be added.
• The system lacks reliable tool outputs.
• You cannot collect traces or run evaluations.
• The team has not defined clear success criteria.

How to measure agentic behavior

You should measure agentic systems at the task level and the step level. A final answer score is not enough because the system may reach the answer through unsafe, expensive, or fragile steps.

Track these metrics:

• Task success rate: Percentage of tasks completed correctly.
• Step count: Number of model calls and tool calls per task.
• Tool success rate: Percentage of tool calls with valid inputs and useful outputs.
• Retry rate: How often the system repeats a failed or low-quality step.
• Escalation rate: How often the system routes to another queue or reviewer.
• Policy violation rate: How often the system breaks a business, safety, or data access rule.
• Cost per successful task: Total model and tool cost divided by completed tasks.
• Latency per task: End-to-end time, including tool calls and retries.

Use LLM observability to inspect these behaviors in traces. You need to see the full path: prompt, model output, tool call, observation, state update, eval result, and final response.

Evaluation strategy for agentic systems

Agentic systems need evals that cover full trajectories, not just final text. A trajectory is the sequence of steps the system took to complete a task.

A practical LLM evaluation setup might include:

• Golden task datasets: Realistic examples with expected outcomes, required tool calls, and unacceptable actions.
• Step-level checks: Assertions for valid tool inputs, correct retrieval targets, and approved action types.
• Final output checks: Grading for accuracy, completeness, tone, format, and policy compliance.
• Regression tests: Runs against previous production failures before each prompt or model change.
• Cost and latency checks: Thresholds for max steps, max tokens, max runtime, and max tool calls.

You can also use LLM-as-a-judge for subjective or hard-to-code criteria, such as whether a support response fully addressed the customer’s issue. Pair judge-based scores with deterministic checks, especially for permissions, schema validity, and tool inputs.

A simple checklist for defining your agent

Before you call your LLM app agentic, write down the following:

1. Goal: What task is the system trying to complete?
2. Allowed actions: Which tools can it call?
3. Decision authority: Which choices can the model make, and which choices stay in code?
4. State: What does the system remember during the task?
5. Memory: What can persist across tasks, if anything?
6. Stop rules: When does the loop end?
7. Budgets: What are the limits for steps, tokens, tool calls, cost, and time?
8. Guardrails: What actions require validation, approval, or rejection?
9. Traces: What data do you record for debugging and review?
10. Evals: How do you test quality before and after release?

This checklist also helps you decide what should live in prompts, what should live in application code, and what should live in your evaluation suite. For complex prompt and tool orchestration, concepts related to an LLM compiler can help teams think about how prompts, tools, and execution plans fit together.

Common failure modes

Agentic systems fail in different ways than single-call LLM apps. Watch for these issues during development and production review.

• Looping: The system repeats the same tool call or planning step without progress.
• Tool misuse: The system calls the wrong tool, passes invalid arguments, or ignores tool errors.
• State drift: The system loses track of what has already happened in the task.
• Over-action: The system takes a write action when it should ask for approval or stop.
• Under-action: The system answers too early without checking required data.
• Hidden cost growth: Small retries and extra planning calls raise cost per task.
• Eval blind spots: The final answer looks fine, but the trace shows unsafe or incorrect intermediate steps.

Start with the smallest useful loop

You do not need a broad, open-ended agent to get value. Start with a narrow loop that handles one task well.

For example, instead of building a general support agent, start with “billing ticket triage for refund requests under $50.” Give the system read access to ticket history and billing status. Let it draft a recommendation. Add approval before any credit is issued. Trace every step. Build a dataset of 100 real or synthetic tickets, then evaluate task success, policy compliance, and escalation accuracy.

Once that works, expand the allowed actions and task types. Each expansion should come with new eval cases, updated guardrails, and trace review.

Bottom line

Define agentic AI by the behavior of your application, not by the model you use. Your LLM app is agentic when it can choose and execute multiple steps toward a goal, observe results, update state, and stop under defined rules.

The production challenge is making that loop reliable. You need versioned prompts, controlled tools, state management, traces, guardrails, and evals. Without those pieces, agentic behavior becomes hard to debug and risky to ship.

PromptLayer helps AI teams manage prompts, trace agent workflows, build datasets, and run evaluations for LLM applications. If you are defining or shipping an agentic LLM app, create a PromptLayer account at https://dashboard.promptlayer.com/create-account.