Agentic AI
エージェント型AI
Agentic AI describes AI systems designed to plan, act, observe, and revise toward a goal. It is often a broader design pattern than a single AI agent implementation.
What it means
Agentic AI refers to AI systems or product designs in which the AI does more than answer a prompt: it interprets a goal, decomposes work, uses tools or information, observes outcomes, and adjusts subsequent actions. The term is broader than a particular AI agent because it includes workflow design, permissions, human checkpoints, evaluation, and auditability. As autonomy increases, product quality depends not only on model capability but also on stop conditions, permission boundaries, data scope, logging, and evaluation sets.
How to calculate it
Evaluate agentic AI by outcome, intervention, safety, and recovery. Autonomous safe completion | Safe tasks completed without human intervention / eligible tasks | Measures useful autonomy Recovery rate | Failed tasks corrected / failed tasks | Measures observe-and-revise behavior Safety deviation rate | Policy violations or unsafe actions / executions | A production-readiness signal
| Lens | Formula / treatment | When to use it |
|---|---|---|
| Autonomous safe completion | Safe tasks completed without human intervention / eligible tasks | Measures useful autonomy |
| Recovery rate | Failed tasks corrected / failed tasks | Measures observe-and-revise behavior |
| Safety deviation rate | Policy violations or unsafe actions / executions | A production-readiness signal |
What counts / what does not
Agentic AI should be controlled autonomy, not unlimited autonomy. Include | Planning, acting, observing, revising, tool use, state management | Agentic behavior Exclude | Unbounded execution, unaudited operations, ownerless decisions | Not acceptable for production Make explicit | Autonomy level, approval points, stop conditions, metrics, logs | Defines operating boundaries
| Item | Treatment | Why it matters |
|---|---|---|
| Include | Planning, acting, observing, revising, tool use, state management | Agentic behavior |
| Exclude | Unbounded execution, unaudited operations, ownerless decisions | Not acceptable for production |
| Make explicit | Autonomy level, approval points, stop conditions, metrics, logs | Defines operating boundaries |
What moves the number
Value increases when long work is decomposed, checked midway, and recoverable. Task decomposition | Turns complex work into testable steps State management | Makes progress and reasons resumable Observation | Uses tool results and errors to change the next step Safety boundary | Approval gates make production adoption safer
| Driver | Metric impact |
|---|---|
| Task decomposition | Turns complex work into testable steps |
| State management | Makes progress and reasons resumable |
| Observation | Uses tool results and errors to change the next step |
| Safety boundary | Approval gates make production adoption safer |
When it helps
Teams can decide whether to expand from one-shot AI features to AI products that progress work over time. Each autonomy level can be mapped to permissions, evaluation, logs, and approvals. Human intervention points can be designed before full automation is attempted.
- Teams can decide whether to expand from one-shot AI features to AI products that progress work over time.
- Each autonomy level can be mapped to permissions, evaluation, logs, and approvals.
- Human intervention points can be designed before full automation is attempted.
How to use it
- Agentic AI includes planning, action, observation, and revision.
- An AI agent is one concrete implementation of an agentic design.
- More autonomy requires stronger permissions, logs, approvals, and evaluation.
- Intermediate checks and recovery paths determine success in long tasks.
- Production adoption should start with safe failure design, not just impressive autonomy.
Decision cautions
The label agentic does not by itself prove mature autonomy. Demos may hide missing exception handling, permissions, and audit logs. Multi-step errors are expensive when detected only at the end. External inputs increase prompt-injection and tool-misuse risk.
- Demos may hide missing exception handling, permissions, and audit logs.
- Multi-step errors are expensive when detected only at the end.
- External inputs increase prompt-injection and tool-misuse risk.
Read with
Read agentic AI with AI agents, tool use, and AI evaluation. AI Agent | Concrete system implementing agentic behavior | Has tools and permissions Tool Use | Ability to operate external systems | Defines action scope AI Evaluation | Measures long-task quality | Supports launch decisions
| Metric | Role | Why read together |
|---|---|---|
| AI Agent | Concrete system implementing agentic behavior | Has tools and permissions |
| Tool Use | Ability to operate external systems | Defines action scope |
| AI Evaluation | Measures long-task quality | Supports launch decisions |
Example
A legal team tests agentic AI for initial contract review. The system reads a contract, extracts risk clauses, compares them with internal policy, and drafts a question list. It is not allowed to edit the contract or send anything externally. Each step cites the relevant clause, and a reviewer approves the findings. Early tests reveal missed clauses, so the team creates an evaluation set by clause category and stores intermediate outputs. The workflow separates what the AI can progress autonomously from what an expert must decide.
Compare with
Agentic AI | Design pattern with planning and action | Fits multi-step work AI Agent | Concrete system implementation | Uses tools and permissions Generative AI | Content generation capability | Often part of an agentic system
| Metric | Difference | Why read together |
|---|---|---|
| Agentic AI | Design pattern with planning and action | Fits multi-step work |
| AI Agent | Concrete system implementation | Uses tools and permissions |
| Generative AI | Content generation capability | Often part of an agentic system |
Common mistakes
- Agentic AI is not necessarily full autonomy. Controlled autonomy is often the better production design.
- Better reasoning alone does not make an agent safe. Tool and permission design are required.
- Longer tasks are not automatically better candidates. Missing intermediate checks increase risk.
Frequently asked questions
Is agentic AI the same as an AI agent?
Not exactly. Agentic AI describes the design pattern; an AI agent is a concrete system using that pattern.
Does it require full automation?
No. Production systems often use controlled autonomy with human checkpoints.
What work fits best?
Multi-step work that has observable intermediate states and a clear rollback or review path.