What Is an AI Agent? Tools, Memory, and Architecture Explained

"AI agent" is used to mean everything from a chatbot with a search button to a fully autonomous software engineer. This guide cuts through the marketing and explains what AI agents actually are architecturally — the reasoning loop, tool calls, memory types, and where the current generation breaks down.

The core idea

An AI agent is an LLM that can take actions in a loop. Instead of generating a single response and stopping, the agent observes its environment, decides what to do, takes an action (usually a tool call), observes the result, and continues until it reaches a goal or gives up.

The simplest possible description:

while not done:
    thought = llm.think(context)
    action = llm.decide_action(thought)
    result = execute(action)
    context.append(result)

Everything else — memory systems, multi-agent orchestration, MCP servers, RAG pipelines — is scaffolding around this loop.

What makes an agent different from a chatbot

A chatbot waits for input, generates a response, and stops. An agent can take a sequence of actions autonomously — searching the web, writing and running code, querying databases, sending emails — without waiting for a human to confirm each step.

The key difference is tool use + loop. Without tools, an LLM is just a text transformer. Without a loop, it's just a single-shot tool. Agents have both.

Tool calls

When an LLM supports tool calling, it can output a structured tool call (name + arguments) instead of plain text. The runtime executes the tool, returns the result, and the LLM continues with that result in context.

A simple tool call exchange looks like:

// LLM decides to call a tool
{
  "tool": "ask_site",
  "arguments": {
    "domain": "stripe.com",
    "question": "How do I handle webhook signature verification?"
  }
}

// Runtime executes the tool, returns result
{
  "content": "Stripe webhooks include a Stripe-Signature header...",
  "sources": ["stripe.com/docs/webhooks#verify-official-libraries"]
}

// LLM continues with this result in context

This is exactly how AgentReady's MCP tools work — when an agent needs to answer a question about a website, it calls ask_site, gets a cited answer back, and incorporates it into its response.

Memory types

Agents have four kinds of memory, each with different characteristics:

In-context (working memory) — Everything in the current context window. Fast to access, directly available to the model, but limited in size and lost when the session ends. Most agents today live primarily here.

External retrieval — Databases, vector stores, document indexes. Unlimited in size. The agent must explicitly retrieve what it needs via tool calls. RAG is the main pattern here: embed a query, retrieve similar chunks, add to context. This is what AgentReady provides for website content.

Episodic memory — Logs of past agent sessions, stored externally and selectively retrieved. Lets an agent "remember" what it did last week. Implemented manually — there's no native LLM feature for this.

Model weights — Knowledge baked into the model during training. The agent knows this without any retrieval. Broad but static — it doesn't know your API, your product, or anything that changed after the training cutoff.

Most agent failures on knowledge tasks come from over-relying on weight memory (the model "knows" an answer that's actually wrong or outdated) rather than retrieving from an authoritative source.

The reasoning loop in practice

Claude Sonnet, when acting as an agent with tool access, follows a loop like this:

1. Receives a task in context (user message + system prompt + previous tool results).

2. Decides whether to call a tool or respond directly. If it needs more information — about an API, a file, the state of something — it calls a tool.

3. The host (Claude Desktop, Cursor, a custom runtime) executes the tool, returns the result.

4. Claude gets the result appended to context and continues. It might call more tools or generate a final response.

This loop runs until the model decides it's done, hits a tool call limit, or runs out of context.

Where agents break down

Context window exhaustion. Long agent loops accumulate tool results, thoughts, and intermediate steps. Eventually the context fills up. The model starts ignoring earlier content or the session fails entirely. Long-horizon tasks are still genuinely hard.

Knowledge cutoff hallucinations. If an agent doesn't have the right retrieval tools, it falls back on weight memory — which is frozen at training time and often wrong for fast-moving topics like API docs. The fix is giving the agent external retrieval tools with authoritative, up-to-date content.

Tool reliability. If a tool returns an error, partial data, or a format the model doesn't understand, the agent may retry incorrectly, give up, or produce a confident but wrong answer. Tool definitions, error messages, and return formats matter a lot.

Goal drift. In complex multi-step tasks, agents sometimes pursue a subtask so hard they lose track of the original goal. System prompt engineering and explicit checkpoints help, but it's still a real failure mode.

Multi-agent systems

Some tasks are too complex for a single agent loop. Multi-agent systems split work across specialized agents: an orchestrator that plans and delegates, subagents that execute specific subtasks, and a synthesis step that combines results.

The protocols for agent-to-agent communication are still maturing. MCP handles tool calls between an agent and a server. Google's A2A protocol attempts to standardize agent-to-agent task delegation. Both are evolving fast.

What this means for your docs and APIs

AI agents are increasingly the first thing people use to answer questions about your product. Not Google, not your docs site — an agent in Cursor, Claude, or a custom workflow asks a question on the user's behalf.

If your docs aren't indexed and queryable, the agent falls back on weight memory — which may be outdated, incomplete, or simply wrong. The agent answers confidently anyway. The user gets a wrong answer and blames your product.

The practical fix: make your documentation available as an external retrieval source. Agents can then call a tool to get the authoritative answer rather than guessing from training data. That's what AgentReady does — crawl, index, and expose your docs as an MCP tool any agent can call.