Short version: An autonomous agent is a model using tools in a loop, deciding its own next step from what it observes. Anthropic calls it an agent; Google calls the core loop ReAct: thought, action, observation. It is the most flexible pattern and the most expensive, so you use it only when you cannot hardcode the path.
What is an autonomous agent?
Agents are systems where the model dynamically directs its own processes and tool usage, keeping control over how it accomplishes a task (Anthropic). That is the line that separates an agent from a workflow: in a workflow, code owns the control flow, but here the model owns it.
Google names the loop that powers this the ReAct pattern, after the 2022 research paper. The agent runs an iterative loop of thought, action, and observation until an exit condition is met (Google Cloud, ReAct paper).
The loop, step by step
Google breaks ReAct into three moves (Google Cloud):
- Thought: the model reasons about the task and decides what to do next, judging whether the request is fully answered.
- Action: it either picks a tool and forms a query to gather more information, or, if the task is done, writes the final answer and ends the loop.
- Observation: it reads the tool output and saves what matters to memory, so it can build on past observations instead of repeating itself.
Anthropic frames the same idea and stresses one thing: at each step the agent must gain ground truth from the environment, such as tool results or code execution, to assess its progress (Anthropic). The loop ends on completion, or on a stopping condition like a maximum number of iterations.
How it actually works
An agent is, in Anthropic's words, typically just an LLM using tools based on environmental feedback in a loop. Because so much rides on those tools, Anthropic spent more time optimizing tools than prompts when building its coding agent, and small changes mattered: switching to absolute file paths removed a whole class of mistakes (Anthropic). Google adds a debugging benefit: the model's thinking gives you a transcript of its reasoning, which helps you see where it went wrong (Google Cloud).
When to use it
Use an autonomous agent for open-ended problems where you cannot predict the number of steps and cannot hardcode a fixed path (Anthropic). The agent may run for many turns, so you need some trust in its decisions. Anthropic's own examples are a coding agent that resolves real GitHub issues across many files, and a computer-use agent that operates a desktop to finish tasks.
Google's fit is the same: complex, dynamic tasks that need continuous planning and adaptation, such as a robotics agent that recomputes its path as new obstacles appear (Google Cloud). If the constraints change while the agent works, the loop lets it adjust.
When not to use it
Do not use an autonomous agent when a workflow will do. If the steps are predictable, a fixed pipeline is cheaper, faster, and easier to trust. Do not use it for high-frequency, low-complexity tasks, where deterministic code beats both workflows and agents on cost and latency. And avoid it in untrusted environments without strong guardrails, because autonomy plus a risky action surface is how small mistakes become big ones.
Known problems
Autonomy has a price. Anthropic is direct: the autonomous nature of agents means higher costs and the potential for compounding errors, so it recommends extensive testing in sandboxed environments with appropriate guardrails (Anthropic).
Google names the two specific risks. The multi-step loop can raise end-to-end latency compared to a single query. And the agent's quality depends heavily on the model's reasoning, so an error or a misleading tool result in one observation can propagate and make the final answer wrong (Google Cloud). One bad observation early can derail everything after it.
Three things to know before you start
- Set a stopping condition. A maximum iteration count keeps a wandering agent from running forever.
- Invest in tools, not just prompts. The agent acts through tools, so a clear, mistake-proof tool interface is where reliability comes from.
- Sandbox first. Test in an isolated environment with guardrails before you let an agent act on anything that matters.
FAQ
What is the ReAct pattern?
ReAct is the reasoning loop behind autonomous agents: the model thinks, takes an action such as a tool call, observes the result, and repeats until it has an answer (ReAct paper).
How is an agent different from a workflow?
In a workflow, the control flow is fixed in code. In an agent, the model decides the control flow at runtime. That flexibility costs more and is harder to debug.
Why do autonomous agents cost more?
They run many turns, each a model call, and every call adds latency and cost. Errors can also compound across steps, requiring more testing.
How do I keep an autonomous agent safe?
Test in a sandbox, add guardrails, set a maximum iteration count, and keep humans in the loop for high-stakes actions.
Sources
- Anthropic, Building Effective Agents: https://www.anthropic.com/engineering/building-effective-agents
- Google Cloud Architecture Center, Choose a design pattern for your agentic AI system: https://docs.cloud.google.com/architecture/choose-design-pattern-agentic-ai-system
- Yao et al., ReAct: Synergizing Reasoning and Acting in Language Models: https://arxiv.org/abs/2210.03629
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