What is an AI Agent?
An AI agent is a system that combines a model with tools, memory and a control loop to take actions toward a goal, rather than just answering a single prompt. It perceives a situation, decides what to do, acts through tools, observes the result and repeats until done. Autonomy ranges from a single tool call to long-horizon execution of complex tasks.
Definition
An AI agent is a system that pairs a reasoning model with tools, memory and an orchestration loop so it can plan and act over multiple steps to achieve a goal.
Key takeaways
- Agent = model + tools + memory + control loop.
- Agents act; chatbots answer.
- Tool use is the bridge to real systems and data.
- More autonomy means more need for guardrails and evaluation.
- Reliability is engineered through the harness, not assumed from the model.
Context
A plain LLM produces text. An agent uses that text to decide and act: it can search, call APIs, write files or trigger workflows, then react to what happens. This loop is what lets it complete tasks instead of merely describing them.
Agents sit on a spectrum of autonomy. Low-autonomy agents make one or two tool calls under tight control; high-autonomy agents run long, branching tasks with little supervision — and need correspondingly stronger guardrails.
Architecture
The core loop is sense → decide → act → observe. The model decides the next action, an orchestrator executes it via a tool, the result is fed back as an observation, and the loop continues until a goal or stop condition is reached.
Around this loop sit memory (to carry state), guardrails (to constrain behavior), and observability (to trace what happened). These are the parts of the harness that make an agent dependable.
Components
Benefits
- Executes tasks end to end.
- Connects models to live systems.
- Recovers from intermediate failures.
- Automates multi-step knowledge work.
Risks
- Error compounding over long tasks.
- Cost and latency without budgets.
- Prompt injection and tool-misuse risks.
- Harder to test and debug than single prompts.
Tools & technologies
Examples
- A triage agent that classifies and routes incoming requests.
- A coding agent that fixes a bug and verifies it with tests.
- A data agent that queries a warehouse and assembles a report.
FAQs
- Is a chatbot an AI agent?
- Not by default. A chatbot answers; an agent takes actions through tools across multiple steps to reach a goal.
- What is the simplest useful agent?
- A model with one or two well-described tools and a clear stop condition. Start simple and add structure only when measurement shows you need it.
- What makes an agent reliable?
- The harness: clean tool design, good memory, guardrails, observability and evaluation — not just a stronger model.
- Single agent or multi-agent?
- Prefer a single agent until a task clearly benefits from specialized, separable roles. Multi-agent adds coordination overhead.