AI agents vs agentic AI: one is the system, the other the category

IBM defines an AI agent as 'a system that autonomously performs tasks by designing workflows with available tools'. In a separate explainer, IBM calls agentic AI 'an artificial intelligence system that can accomplish a specific goal with limited supervision', made up of AI agents whose work is coordinated through orchestration when there are several of them.

OpenAI's practical guide to building agents is stricter about the agent itself: 'Agents are systems that independently accomplish tasks on your behalf.' It adds that applications which call a model without letting it control the flow of work, such as simple chatbots, single-turn models or sentiment classifiers, are not agents.

Anthropic draws the line in a different place. Its article Building effective agents (December 2024) uses 'agentic systems' as the umbrella term and splits it in two: workflows, where models and tools follow predefined code paths, and agents, where the model directs its own process and tool use. On that reading, a fixed pipeline with a model inside it is agentic but is not an agent.

Both terms describe the same building blocks: a model, tools it can call, and instructions. What changes is scope. 'AI agent' names one thing you can deploy, test and give a name, such as a refund agent or a coding agent. 'Agentic AI' names a property of a system, or a whole class of systems: it pursues a goal and decides some of its own steps.

That is why marketing uses the two terms interchangeably, and why the difference matters when you buy. A vendor selling 'agentic AI' may be selling one agent, a multi-agent setup, or a workflow with a model call in the middle. Ask which of the three it is, because each one fails in a different way.

A supplier emails to dispute an invoice. A single AI agent could read the email, look up the invoice and purchase order through two tools, draft a reply and propose a credit note. It picks the order of those steps itself and stops when it judges the dispute understood.

An agentic system for the same job might split the work. A triage agent classifies the email, a finance agent checks the ledger, and a workflow with fixed steps sends any credit above a set amount to a named approver before anything posts. The system as a whole is agentic. Only two of its parts are agents, and the approval route is ordinary workflow logic.

Anthropic advises starting with the simplest solution and adding multi-step agentic behaviour only when simpler solutions fall short. That gives a sensible order: one model call with good context first, then a workflow with fixed steps, then one agent, and several agents only when a single agent becomes too hard to instruct and test.

• Build one agent when a single task needs judgement over which tools to call and in what order.
• Build a workflow when you can write the steps down in advance and they rarely change.
• Build a multi-agent system when separate parts of the job need different tools, permissions or instructions.

A single agent fails by taking a wrong step and building on it. Anthropic names higher costs and 'the potential for compounding errors' as the price of agent autonomy, and recommends testing in sandboxed environments, guardrails, and stopping conditions such as a maximum number of iterations.

A larger agentic system adds hand-off failures: one agent passes a half-finished result to the next, and nobody can tell which part decided what. OpenAI's guide recommends handing control to a person when an agent exceeds its retry limits or reaches a high-risk action such as a large refund or a payment. Both kinds of failure call for the same remedy: a trace of each step and a person at the consequential ones.

Studio builds both shapes: agents that choose their own tool calls, and workflows with fixed steps. A workflow can call any model reachable through Connect, call tools through integrations, and pause at an approval step until a named person decides. That lets you put a scripted gate around an agent instead of trusting the agent to stop itself.

Nexus puts policy, approval and audit around coding agents and agent runtimes. Two honest limits: approvals exist in several parts of the platform and are not one unified inbox, and the L1 to L5 autonomy levels described on this site are designed, not built.

• Swfte Studio: Build agents and workflows with approval steps
• Swfte Nexus: Policy, approval and audit for agent runtimes
• Agents on the platform

Facts about other vendors and about the terms on this page were read on the pages below on 7 October 2026. Vendor plans, names and menus change, so check the vendor’s page before you rely on a detail.