Agentic AI vs. generative AI

Generative AI produces content. Agentic AI uses models inside a system that can pursue a goal, select steps and interact with tools or other systems. The same language model may appear in both. The important difference is not the model label. It is whether the surrounding application can take action and how that authority is controlled.

NIST describes agentic AI in terms of autonomous agents that make decisions, adapt and interact with users, systems and real-world scenarios. NIST's adversarial machine learning taxonomy also notes that large language models can be extended through retrieval and tool use to take real-world actions. That boundary changes what an enterprise needs to evaluate.

The short comparison

Question	Generative AI	Agentic AI
Primary job	Produce or transform content	Move a goal through a sequence of decisions and actions
Typical output	Text, code, image, summary or classification	A plan, tool calls, state changes and a final result
System access	Often limited to the prompt and retrieved context	May include APIs, databases, files, browsers or other agents
Human role	Reviews the generated output	Sets authority and may approve consequential actions during execution
Main control need	Input handling, grounding, output review and data protection	All generative controls plus tool authorization, action limits, approvals, interruption and audit
Evidence to retain	Prompt and context references, model version and output	The full decision and action path, including policy and approval outcomes

These are system patterns, not mutually exclusive product categories. An agent can use a generative model to draft a message, classify a document or explain a plan. It becomes agentic when the application uses those outputs to choose and execute subsequent steps under some degree of autonomy.

A generative workflow stops at the output

Consider a model asked to draft a procurement summary. It reads the supplied text and returns a draft. A person decides whether to copy, edit or send it. The model can be wrong, disclose sensitive context or follow a malicious instruction in the source material, but the output does not change the procurement system by itself.

The control boundary centers on what the model can read and what people do with the result. Useful checks include source grounding, prompt-injection defenses, sensitive-data handling, model and prompt versioning, and a clear review step before publication or reliance.

An agentic workflow continues into tools

Now consider a system that retrieves the package, identifies missing material, drafts the summary, updates a record and prepares a submission. The model is still generating content, but the surrounding loop can observe state, choose a tool, provide arguments, interpret the result and continue.

That creates several new failure paths:

• The agent may select a tool it does not need.
• A retrieved document may contain instructions that steer the agent away from the user's intent.
• A valid tool may receive parameters that exceed the approved scope.
• An action may be individually permitted but unsafe after several earlier actions.
• A human approval may describe one set of parameters while execution uses another.
• A failed or looping run may consume cost, hold locks or repeat external actions.

The OWASP AI Agent Security Cheat Sheet recommends least privilege, explicit approval for high-impact actions, structured outputs, limits on recursion and tool chains, and adversarial testing after changes to prompts, tools, memory, retrieval, policies or model providers. These are application controls around the model, not traits the model can supply on its own.

Autonomy should be bounded by the action

An enterprise does not need one global setting called autonomous. A safer design assigns authority according to the action and context.

Read-only retrieval from an approved source may run automatically. Creating a reversible internal draft may require policy checks but no interruption. Sending an external message, committing funds, deleting a record or changing access should normally require a parameter-bound approval from a named person. The approval should show the exact action, target and material arguments, then expire or become invalid if those parameters change.

This is where a control plane becomes useful. It sits between a model's proposed action and the tool that could carry it out. The control plane can evaluate identity, tenant, data classification, requested operation, rate and approval state before dispatch. It can also record the decision even when the request is denied.

The model should never be the final authority on whether its own action is allowed. Policy enforcement belongs in deterministic application code and the downstream system's own permission boundary.

Evaluation changes when a system can act

A text-generation evaluation asks whether the output is accurate, relevant, safe and useful. An agent evaluation needs those measures plus evidence about the path taken.

Test at least these behaviors:

1. Task success. Does the system complete the intended job against representative cases?
2. Authorization. Are disallowed tools, records, tenants and operations denied at execution time?
3. Approval integrity. Can a high-impact action run without a valid approval, after approval expires or with changed parameters?
4. Injection resistance. Can untrusted content redirect tool use or expose data outside the original scope?
5. Recovery. Can an operator interrupt the run, understand partial work and safely resume or roll back?
6. Limits. Do bounded retries, chain depth, time and cost stop runaway behavior?
7. Auditability. Can a reviewer reconstruct the model, policy, tool, approval and result for each consequential step?
8. Change control. Are the tests repeated after material changes to models, prompts, tools, retrieval or policy?

The NIST AI Risk Management Framework organizes AI risk work around Govern, Map, Measure and Manage. It is voluntary and broader than agent security, but its lifecycle view is useful here: a successful demonstration is not a substitute for continuing measurement and management after deployment.

When to use each pattern

Use a generative pattern when the value is the artifact and a person can reasonably review it before anything else happens. Drafting, summarization, transformation and assisted analysis often fit this shape.

Use an agentic pattern when value depends on coordinating steps across systems or maintaining progress through a workflow. The additional autonomy should earn its place by reducing real operational handoffs, not by making a simple generation task harder to inspect.

Some workflows should remain partly agentic. Let the system gather context, propose a plan and prepare changes, then stop at a human decision boundary. Autonomy is not all or nothing. The right level is the smallest authority that completes the job safely.

Questions for an enterprise buyer

Before approving an agentic system, ask:

• Which tools and data can each agent reach?
• Who or what grants that access, and is it checked again for every action?
• Which actions always require a person?
• Does approval bind the exact target and parameters that will execute?
• How are untrusted documents, web pages and tool results isolated from instructions?
• What stops loops, repeated writes and runaway cost?
• Can an operator interrupt and recover a partially completed run?
• What record proves what the system proposed, allowed, approved and executed?
• Which claims are architectural goals, and which have test evidence for this release?

Trunnion AI describes its approach as governed agentic software because the design centers on policy, bounded tools, human authority and reviewable execution. The controls available in a specific product remain release and deployment specific. Start with the Trunnion AI control-plane overview, then use the enterprise agentic AI glossary entry and hash-chained audit-trail guide to frame a technical evaluation.