An AI agent is not magic, and it is not just a pile of if-statements with a large bill attached. The real difference is who decides the next step. In a scripted system, a programmer decides in advance. In an agent, a model chooses the next action while the task runs and adjusts based on what happened. This article explains how that loop works, what makes it autonomous, how it differs from a chatbot, and where the claims stop being supported.
The short answer: what an agent changes
Anthropic defines an agent as an AI model that directs its own processes and tool use to accomplish a task. It decides how to reach the user’s goal instead of following a fixed script (Anthropic, “Trustworthy agents in practice”). The if-statement joke is half right. Both a script and an agent branch, but a script’s branches are written by a person before the run. An agent’s branches are chosen by a model during the run, from the results it sees.
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That is a difference in control flow. It does not mean agents contain no ordinary code or rules. Most real agents are surrounded by plenty of both.
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Anthropic describes the behavior as a self-directed loop: plan, act, observe the result, adjust, and repeat until the task is done or a human is needed. Each pass uses the outcome of the previous action, such as a search result, an error message, or a changed file, to choose the next one.
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Four components, not one
Anthropic’s description splits an agent into four parts:
- Model: the AI that reasons and decides.
- Harness: the instructions and guardrails around the model.
- Tools: the services and applications the model can use.
- Environment: where the agent runs and which data and systems it can reach.
The practical consequence is that the same model can behave very differently when its permissions, tools, or accessible environment change. An agent that can only read documents and one that can send email or modify a database share a model but not a risk profile. “How capable is the model?” is only half of the question. The other half is what the system around it allows.
A research illustration: RAFA
One published way to build the loop is RAFA, “Reason for Future, Act for Now” (Liu et al., Proceedings of Machine Learning Research, 2024). It prompts an LLM to plan a longer trajectory using a memory buffer. The agent performs only the next action, stores the feedback, and then reasons again to replan from the updated state. The authors’ theoretical analysis establishes a regret bound that scales with the square root of T. That is a mathematical result about their framework under their assumptions, not a performance guarantee for agents in general. RAFA is one approach to combining planning, memory, and feedback, and not every agent works this way.
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Fixed workflow versus agent: a spectrum
Treat this as a spectrum rather than a binary. This framing is an editorial synthesis, not a claim that one side always wins.
| Axis | Fixed workflow | Agent loop |
|---|---|---|
| Control | Predetermined sequence and branches | Adaptive planning and replanning by the model |
| State | Usually no retained feedback beyond what code passes along | Memory or state used in later decisions |
| Action surface | Only the steps a developer wired in | Any tool the harness exposes, from read-only to systems that change data |
| Oversight | Human review is built into the design of the steps | Approval at every action, only for consequential actions, or broad delegated discretion |
| Best fit | Predictable, bounded steps | Tasks whose path depends on intermediate results |
Deterministic branches stay valuable where the steps are known, because they are easier to test and predict. An agent loop earns its added cost and variability when you cannot know the path in advance.
What makes an agent “autonomous”?
Autonomy here is a property of a system, not of a model’s character. A system is more autonomous when:
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- the model, not the developer, picks which action comes next;
- it can use tools to affect or read the outside world;
- it uses observed results to change course; and
- it can continue without a human approving each step.
It does not follow that the model is independently reliable, or “truly autonomous” in a human sense. Autonomy is granted by the harness, tools, and environment, and it can be narrowed or widened by changing them.
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Agents versus chatbots
A chatbot answers a message and waits. An agent takes a goal and works toward it across multiple steps, using tools and checking results. The line blurs, because a chatbot with search or code tools can run a small loop. The test is whether the system chooses and executes a sequence of actions toward a goal, or only produces a reply to the current prompt.
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Not automatically. Anthropic’s June 13, 2025 engineering article, “How we built our multi-agent research system,” describes an orchestrator-worker design. A lead agent coordinates specialist agents working in parallel. The company says this helps with open-ended research, where next steps are hard to predict. It reported a 90.2% improvement over single-agent Claude Opus 4 on its own internal research evaluation, with Claude Opus 4 as lead and Claude Sonnet 4 as subagents. That figure applies to that evaluation and configuration only, and it is company-reported. The same article names coordination, evaluation, and reliability as real challenges.
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What a modular-planning study shows, and what it doesn’t
A 2025 Nature Communications paper, “A brain-inspired agentic architecture to improve planning with LLMs,” tested a modular system called MAP. It reported:
- An average of 74% of standard three-disk Tower of Hanoi problems solved, against 11% for GPT-4 zero-shot.
- 31% invalid moves when the monitor module was ablated (removed), versus 0% for the other ablation models in the reported comparison.
The authors argue that monitoring, tree search, and task decomposition each contributed. The lesson is narrow: on a structured puzzle, splitting planning into checked components can reduce errors. A puzzle is not a proxy for open-ended real-world work, and these numbers are not general agent success rates.
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Risks grow with autonomy
Because agents act with less human oversight, Anthropic identifies three risks: misread intent, unintended consequences, and prompt injection, where malicious instructions hidden in content the agent reads try to redirect it. Its principles for trustworthy agents are human control, alignment with human values, secure interactions, transparency, and privacy.
Since an agent is a system, the model is only one defensive layer. A strong model inside a weak harness, with overly permissive tools or an exposed environment, can still do damage. OpenAI’s December 14, 2023 paper, “Practices for Governing Agentic AI Systems,” frames agentic AI as systems pursuing complex goals with limited direct supervision. It proposes baseline responsibilities and safety practices, and it says openly that some operational uncertainties must be resolved before practices can be codified. Read it as 2023-era governance framing, not a settled standard.
A practical checklist for judging an agent
- Control: what does the model decide, and what is fixed in code?
- State: what feedback does it keep, and how does it use it?
- Action surface: can it only read, or can it change external systems?
- Oversight: which actions need human approval?
- Evaluation: is success measured on your task, including invalid actions, recovery from errors, and cost and latency?
- Deployment context: which data and permissions does it actually get? These set both its usefulness and what can go wrong.
The Bottom Line
An agent is an expensive if-statement only in the sense that it still branches. What is new is that a model picks the branch at run time, from feedback. Whether that is worth the extra cost and risk depends less on the model’s cleverness than on the harness, tools, permissions, and evaluation you build around it.
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