What is agentic AI?
Agentic AI is AI that pursues a goal with limited supervision, planning steps and calling tools. Sources disagree on how it differs from an AI agent.
Agentic AI is AI that works toward a goal with limited human supervision: it plans steps, uses tools such as APIs, search or code, and adjusts based on what comes back. IBM defines it as “an artificial intelligence system that can accomplish a specific goal with limited supervision.” There is no single agreed definition, and the main disagreement is how it relates to an “AI agent”.
How major sources define it
| Source | Definition | Relation to AI agents |
|---|---|---|
| IBM | A system that “can accomplish a specific goal with limited supervision”; shows autonomy, goal-driven behavior and adaptability | Agentic AI “consists of AI agents” |
| Stanford HAI | “AI systems designed to act as autonomous or semi-autonomous agents”; they set or interpret goals, plan, use tools and adapt from feedback | Defined in terms of agents |
| AWS | “An autonomous AI system that can act independently to achieve pre-determined goals” | “Typically builds upon multiple hyperspecialized agents” |
| MIT Sloan (2026-02-18) | “AI systems that are semi- or fully autonomous” and able to perceive, reason and act | Sinan Aral: agentic AI is the broader category of “multiple, different agents that are orchestrating a task together” |
| Anthropic (2024-12-19) | “Agentic systems” cover both workflows and agents | Separates the two by who controls the steps |
All five agree on three parts: a goal, tool use, and less supervision than a chatbot needs. They differ on whether the word names a single system or a multi-agent setup. When you read a vendor page, check which meaning it uses.
Workflows and agents
Anthropic draws the line most useful to someone building a system. “Workflows are systems where LLMs and tools are orchestrated through predefined code paths.” Agents are “systems where LLMs dynamically direct their own processes and tool usage”. Both sit on the same building block, “an LLM enhanced with augmentations such as retrieval, tools, and memory.”
| Fixed workflow | Agent | |
|---|---|---|
| Who picks the next step | Your code | The model, at run time |
| Anthropic’s guidance | “predictability and consistency for well-defined tasks” | Better “when flexibility and model-driven decision-making are needed at scale” |
| Patterns Anthropic names | Prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer | An LLM loop that uses tools and checks results |
| Cost | Lower | “The autonomous nature of agents means higher costs, and the potential for compounding errors” |
Source for the table: Anthropic (December 2024; Anthropic notes the tooling it describes has changed since, but the workflow and agent split still holds). Its advice is to find “the simplest solution possible” and to add complexity only when needed. For many applications, it says, “optimizing single LLM calls with retrieval and in-context examples is usually enough.” See agentic workflows for the fixed-path side.
Agentic AI and generative AI
Generative AI creates content from a prompt. IBM says agentic AI “extends this capability by applying generative outputs toward specific goals”, and notes that LLMs on their own cannot call external tools or databases while agents can (IBM). Stanford HAI contrasts agentic AI with “a purely reactive chatbot that only responds turn-by-turn” (Stanford HAI). MIT Sloan describes agents as going beyond answers to taking actions and executing multistep workflows across other systems (MIT Sloan).
Risks that come with autonomy
MIT Sloan names three risks: unreliable or unethical decisions (it gives a loan rejected on faulty information as an example), cybersecurity and the need for permission controls, and unclear accountability. It also notes that agents struggle with exceptions and that value is hard to prove without shared metrics. Katherine Kellogg warns that “Just because an agentic AI model reclaims 20% of someone’s time, that doesn’t mean it’s a 20% labor-cost savings” (MIT Sloan).
Anthropic’s advice for agents is to have them get “ground truth” from the environment at each step, include “stopping conditions (such as a maximum number of iterations)”, and recommends “extensive testing in sandboxed environments, along with the appropriate guardrails” (Anthropic). For guardrails, see AI guardrails.
Measuring an agentic system
Because the model chooses the steps, one run can differ from the next, so checking a single answer is not enough. Record the whole task as one trace and score the outcome and the path. Agent observability covers what a trace should hold and how loops, tool errors and cost show up. LLM evaluation covers scoring methods. Tool connections often run over the Model Context Protocol.
Agentic AI in HoneyHive
HoneyHive records each agent run as a session of nested model, tool and chain events, which you can then score with evaluators. The Trajectory view shows the steps of a run on one grid, so repeated tool calls are easy to spot.
FAQ
What is the difference between agentic AI and generative AI?
Generative AI produces content such as text or images from a prompt. IBM says agentic AI "extends this capability by applying generative outputs toward specific goals" (IBM). An agentic system usually runs a generative model inside a loop that also calls tools.
Is agentic AI the same as an AI agent?
Sources split. MIT Sloan quotes Sinan Aral separating AI agents from "the broader category of agentic AI", which he defines as systems of "multiple, different agents that are orchestrating a task together" (MIT Sloan). IBM and AWS describe agentic AI as built from AI agents. Anthropic avoids the split and calls workflows and agents both "agentic systems" (Anthropic).
When should I not build an agentic system?
When a single model call with retrieval and examples is enough. Anthropic says that is "usually enough" for many applications, and that agentic systems "often trade latency and cost for better task performance" (Anthropic).
How do you test an agentic system?
Test the whole task, not only single answers, and run it in a sandbox with guardrails and a stopping condition. See agent observability for what to record and LLM evaluation for scoring.