An AI agent is a system that uses a language model to decide what actions to take in pursuit of a goal, executes those actions, observes the results, and repeats until the goal is reached or the system determines it cannot proceed. This is distinct from a simple chatbot, which responds to a single prompt without taking external actions.
What makes something an "agent"?
The word "agent" is overused in AI marketing, but it has a meaningful technical definition. An agent:
- Has a goal or task — not just answering a question, but accomplishing something
- Can take actions — calling APIs, running code, searching the web, writing files
- Observes results — sees what happened as a result of each action
- Plans across multiple steps — does not just respond once, but reasons through a sequence of decisions
The language model is the "brain" that interprets results and decides what to do next. Tools — functions the model can call — are the "hands" that interact with the world.
The basic agent loop
Most agent systems follow a similar pattern:
1. Receive a task
2. Think about what to do next (LLM call)
3. Take an action (tool call)
4. Observe the result
5. If done → return final answer
If not done → go to step 2
This is sometimes called the ReAct pattern (Reasoning + Acting), described in a 2022 paper that showed language models could interleave reasoning and action effectively.
Tools: what agents can do
The capabilities of an agent depend entirely on what tools it has access to. Common tools include:
- Web search: Retrieve current information from the internet
- Code execution: Run Python, JavaScript, or other code and return results
- File system access: Read and write files
- API calls: Interact with external services
- Database queries: Retrieve or store structured data
- Browser control: Navigate websites, fill forms, extract content
Each tool is defined by a name, description, and parameter schema. The model receives these definitions and decides which tool to call and with what arguments.
Planning and memory
Simple agents operate with just the conversation history as memory. More sophisticated agents may use:
- External memory: Storing facts in a vector database and retrieving them when relevant
- Working memory: A scratchpad where the agent can write down intermediate results
- Long-term memory: Persistent storage across sessions
Planning in agents can be implicit (the model decides what to do next at each step) or explicit (the model first creates a plan, then executes it). Explicit planning tends to work better for complex multi-step tasks but is slower and more expensive.
Multi-agent systems
A single agent has limits — it can only handle tasks that fit within a single context window, and it cannot work on multiple things simultaneously. Multi-agent systems address this by having agents delegate to sub-agents.
A common pattern is an orchestrator + worker architecture: a high-level agent breaks a task into subtasks and assigns them to specialized worker agents. The worker agents report results back to the orchestrator, which synthesizes them.
This is more complex to build and debug but enables parallelism and specialization.
Why agents are unreliable (and what to do about it)
Agents are harder to build reliably than they appear. Common failure modes:
Context accumulation: As the agent loop runs, the context grows. After many steps, important early information may be lost or diluted.
Tool call errors: The model may call a tool with incorrect parameters, interpret results incorrectly, or get stuck in a loop.
Goal drift: The model may lose track of the original goal and pursue a related but different objective.
Overconfidence: The model may confidently take an irreversible action (deleting a file, sending an email) based on a mistaken interpretation.
Mitigation strategies include:
- Human-in-the-loop checkpoints for irreversible actions
- Limiting the number of steps
- Structured output formats that are easier to validate
- Testing agents extensively on representative tasks
Current state of the technology
As of 2025, AI agents are genuinely useful for:
- Research tasks (gather information, summarize, report)
- Code generation and debugging on bounded problems
- Data processing pipelines
- Workflow automation with well-defined steps
They are less reliable for:
- Complex long-horizon planning
- Tasks requiring nuanced judgment
- Anything where errors have significant real-world consequences
The technology is improving rapidly, but agents still require careful design, thorough testing, and appropriate human oversight.
Summary
AI agents combine language models with tools to accomplish multi-step tasks. The core loop is: reason about what to do, take an action, observe the result, repeat. Their capabilities depend on what tools they can access. They are genuinely useful but require careful design to be reliable — especially for tasks involving irreversible actions or complex planning.