Concepts
Agentic models
1 · In one line
An agent plans, acts, observes the result, adjusts its approach, and repeats until the task is complete.
1 · What it is
In an agentic system, the model controls its process and tool use. Once the task is clear, the agent plans and operates independently.
The agent acts and observes the result. It adjusts its approach and repeats until it has completed the task.
Anthropic suggests stopping conditions to maintain control. Agents may return to a human for further information or judgement.
Agentic models coordinate decisions and tool use across several steps.
Choose actionsAn agent uses a language model to manage workflow execution and make decisions.
Use toolsTools extend an agent's capabilities by using application or system APIs.
Adapt the planA self-directed loop can plan, act, observe the result, and adjust what happens next.
Follow one goal through an observe-and-act loop.
- 1 · receiveGive the agent a command or clarify the task through discussion.
- 2 · decideLet the model manage workflow execution and make decisions.
- 3 · observeThe agent observes the result and adjusts its approach.
- 4 · stopUse a stopping condition such as a maximum number of iterations.
Guardrails are critical for an LLM deployment.
| Who | What they ask | What it works with |
|---|---|---|
| Operations team | “Can the system complete a multi-step task across connected tools?” | Tool-driven workflow |
| Agent developer | “Which observation should determine the next action?” | Agent loop state |
| Risk owner | “Which actions require a human checkpoint?” | Guardrail boundary |
solves
- Agents can plan and complete tasks using tools.
- ReAct interleaves reasoning traces and task-specific actions.
- Agents can maintain context across steps.
doesn't solve
- Anthropic suggests stopping conditions such as a maximum number of iterations.
- Sensitive actions can require human oversight.
- Anthropic says agents should obtain ground truth from their environment at each step.
6 · Go deeper
Sources used
This explainer is written in original language. The links below support its factual claims.
- officialA practical guide to building agents, OpenAI · read 28 Sept 2026
- officialBuilding effective agents, Anthropic · read 28 Sept 2026
- officialMeasuring progress on scalable oversight for large language model agents, Anthropic · read 28 Sept 2026
- paperReAct - Synergizing Reasoning and Acting in Language Models, Yao et al. · read 28 Sept 2026
- officialAgents guide, OpenAI · read 28 Sept 2026