AI Agents, Explained in Detail

First, retire the marketing

"AI agent" has been stretched until it means almost anything. Strip the buzz and the definition is precise: an agent is a program that uses a model to make decisions in a loop, acting on its environment, observing the result, and repeating — until a goal is met or it gives up.

A chat completion is not an agent. It is one inference. An agent is the loop around the inference.

The agentic loop

Every agent, from a simple web search tool to a multi-day engineering workforce, runs the same cycle:

1. Perceive — gather state from the environment (tool results, files, logs).
2. Decide — ask the model what to do next, given the goal and current state.
3. Act — execute the chosen action through a tool.
4. Observe — capture the outcome and feed it back in.

async function runAgent<T>(goal: string, execute: (action: AgentAction) => Promise<Observation>) {
  const messages: Message[] = [{ role: "system", content: systemPrompt(goal) }];

  for (let step = 0; step < MAX_STEPS; step++) {
    const decision = await model.complete(messages);       // decide
    if (decision.finished) return finalize(decision);      // goal met

    const observation = await execute(decision.action);    // act + observe
    messages.push(decision.message, observation.message);  // perceive next
  }

  throw new Error("step limit exceeded");
}

The whole field is, in one form or another, an optimization of this loop: better perception, better decisions, safer actions.

The three capabilities that make it work

An agent is not the model. It is the combination of three things:

• Tools — the agent's hands. Search, file IO, shell, HTTP, databases. A model without tools can only produce text; an agent with tools produces change in the world.
• Memory — context that persists across turns. Short-term (the running conversation), long-term (a knowledge base, past runs), and episodic (records of what worked before). Without memory, every turn starts from zero.
• A planning layer — the ability to break a goal into steps. Simple agents plan implicitly inside the model's reasoning. Reliable agents plan explicitly, with checkpoints a human can inspect.

An agent is only as trustworthy as the sandbox it runs in. Capability without containment is just a faster way to break things.

Where they actually add value

The agents that earn their keep share a profile:

• The task has a clear success condition that can be checked.
• The environment is observable — actions produce results you can read.
• Failure is recoverable — a bad step can be rolled back or retried.
• The cost of iteration is acceptable.

Code generation, test writing, data cleanup, document analysis, and researcher-style information gathering all fit. Open-ended creative work and any task where a mistake is expensive and irreversible do not — yet.

Design principles for a reliable agent

From building them in anger, these are the rules that hold up:

1. Constrain the loop. A step limit, a time budget, and an explicit stop condition. An agent that cannot conclude is not smart, it is stuck.
2. Make tools small and typed. Each tool is a function with a schema. Validation on input, structured output. A tool that can return anything is a hallucination waiting to happen.
3. Log the loop. Record every decision, action, and observation. The debug story of an agentic system is the trace, not the final answer.
4. Human checkpoints on irreversible actions. Delete, pay, deploy, merge — gate them. Autonomy is a dial, and you control where it stops.
5. Let it verify its own work. Give the agent a way to run tests against its output. Self-verification converts "probably right" into "checkable".

const toolSchema = {
  name: "run_tests",
  input: { type: "string", description: "test command" },
  output: { type: "string", description: "test output" },
};

What to watch out for

The failure modes are consistent across every implementation:

• Confabulation of tool results. The model believes it called a tool it never did. Always execute tools deterministically and show the real output.
• Loops. The same action, the same failure, forever. Detect repeated (action, observation) pairs and force a strategy change.
• Context bloat. Every observation is tokens. Long agents run out of attention; summarization and retrieval are not optional at scale.
• Brittle evaluation. A demo that passed once teaches you nothing. Measure over a test set with pass/fail checks, and track regressions.

The honest summary

An AI agent is a decision loop wrapped around a model, amplified by tools and memory, and bounded by engineering. The models improve every quarter; the engineering — sandboxes, schemas, observability, checkpoints — is what separates a demo from a system people trust.

Build the loop small, make the tools honest, and gate the irreversible. The magic is real, but it lives in the plumbing, not the hype.