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Glossary

What is agentic AI?

Agentic AI describes AI systems that pursue a goal with a degree of autonomy: they plan the steps, use tools, check their own work and act, often with several agents working together and people approving what matters.

Written and reviewed by Solorai

In short

Where a chatbot answers one question, an agentic system works towards an outcome. Google Cloud describes agentic AI as a form of AI focused on autonomous decision-making and action, and Anthropic uses the term agentic systems for both workflows that follow coded paths and agents that direct themselves.

Larger agentic systems use an orchestrator agent that splits a goal into tasks for specialised agents, combines their results and asks a person to approve the steps that carry risk.

Agentic AI and AI agents

The two terms overlap. An AI agent is a single system that runs a loop of deciding, acting and checking. Agentic AI is the wider approach: designing AI systems, from simple workflows to many cooperating agents, that take actions towards a goal rather than only producing a reply.

Autonomy is a dial, not a switch. Anthropic’s guide to building effective agents describes the settings along it:

  • Prompt chaining: a task is split into fixed steps, each handled by a model call.
  • Routing: an input is classified and sent to the right specialised step.
  • Parallelisation: independent parts run at the same time and are combined.
  • Orchestrator and workers: a model breaks the task down while it works and delegates the parts.
  • Evaluator and optimiser: one model drafts, another checks and asks for changes.
  • Agents: the model plans and uses tools on its own until the task is done.

The guide’s advice is to choose the simplest option that works, and to add autonomy only where it pays for itself.

How multi-agent systems work

In a multi-agent system an orchestrator agent receives the goal, makes a plan and hands tasks to specialised agents: one researches, one drafts, one reviews. Each has its own instructions, tools and context, so it can focus on one job. The orchestrator collects the results, resolves conflicts and decides what happens next.

The design has clear benefits: parallel work on large tasks, a separation between the agent that does and the agent that checks, and shorter, clearer instructions for each part. It also has costs: more model calls and therefore more spend, more time per task and more places where things can go wrong. A single agent is often enough. Several agents earn their place when a task splits naturally into parts.

People stay in the loop at defined points: approving the plan, signing off before anything is sent, paid or changed, and reviewing a sample of finished work.

Examples of agentic AI in companies

  • Bid responses: one agent pulls the requirements from the tender, another drafts answers from past proposals, a third checks them against the requirements.
  • Due diligence: agents read a data room in parallel, extract issues by topic and assemble a report for the deal team.
  • Month-end support: agents reconcile accounts, explain variances and prepare a pack for the controller.
  • Software changes: coding agents such as Claude Code plan a change, edit the code, run the tests and open a pull request for review.

What the research says

  • Gartner expects 40% of enterprise applications to include task-specific AI agents by the end of 2026, up from less than 5% in 2025.
  • Gartner also expects over 40% of agentic AI projects to be cancelled by the end of 2027, because of rising costs, unclear business value or inadequate risk controls.
  • In McKinsey’s State of AI 2026 survey, about two in ten respondents said their organisation had reached the scaling phase with AI agents.

Read together, the figures point the same way: the technology is arriving quickly, and the projects that last have a measurable goal and proper controls from the start.

How to start with agentic AI

See how we approach agentic AI consulting.

Sources

FAQ

Questions about agentic AI

What is the difference between agentic AI and generative AI?

Generative AI produces content, such as text, images or code, in response to a prompt. Agentic AI uses generative models to pursue a goal across several steps: it plans, uses tools and acts in other systems.

What is the difference between agentic AI and AI agents?

An AI agent is one system that decides and acts through tools. Agentic AI is the wider approach, which ranges from simple workflows with a model in each step to several agents working together under an orchestrator.

What is a multi-agent system?

A design in which an orchestrator agent splits a goal into tasks for specialised agents, such as research, drafting and review, then combines their work. It suits tasks that divide naturally into parts.

Is agentic AI safe for companies?

It can be, with the right controls: narrow access to systems, approval before actions with consequences, limits on steps and spend, and a log of every action. Gartner names inadequate risk controls among the reasons it expects many agentic AI projects to be cancelled.

Where should a company start with agentic AI?

With one process that has a measurable outcome, the simplest design that could work, and a pilot on real cases measured against a baseline.

Related reading

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