How AI Agents Work: Tools, Memory, and Planning

Introduction

Ask ten people what an "AI agent" is and you'll get ten different answers. Some mean a chatbot with a system prompt. Others mean a fully autonomous system that plans, executes, and corrects its own work over dozens of steps. The difference matters, because the engineering required to go from "answers questions" to "gets things done" is substantial.

This article breaks down the actual mechanics behind AI agents: how they decide what to do next, how they call external tools, how they remember what happened earlier in a task, and where the architecture tends to break down in practice. If you've read about the conceptual distinction between AI agents and AI assistants, this is the layer underneath: what's actually running when an agent works through a task. Once you've got the mechanics down, the companion guide on building your first AI agent walks through putting them into practice.

What Is an AI Agent?

An AI agent is a system built around a large language model that can take actions in a loop, observe the results, and decide what to do next - without a human specifying each step in advance.

A plain LLM call is single-shot: input goes in, text comes out, done. An agent wraps that call in a loop with three additional capabilities:

• Tool use - the ability to call functions, APIs, or external systems (search the web, run code, query a database, send an email) rather than only generating text.
• State and memory - a way to carry information forward across multiple steps, so the agent doesn't "forget" what it already tried or found.
• Planning and control flow - logic that decides whether the task is finished, what to try next, or whether to ask for help.

None of these require exotic technology. Most production agents today are built from an LLM, a set of tool definitions, and a loop written in ordinary application code. The intelligence is in the model; the reliability is in the engineering around it.

The Core Agent Loop

Most agent architectures - regardless of framework - reduce to a variation of the same loop:

1. Observe. Gather the current state: the user's goal, prior steps taken, and any new information (a tool result, a file, an error message).
2. Think. The model reasons about what to do next given the goal and the current state. This is often implemented as a "reasoning" step where the model explains its plan before acting.
3. Act. The model selects a tool (or decides to respond directly) and generates the arguments for that call.
4. Execute. The application code actually runs the tool call - hitting an API, querying a database, executing code - and captures the result.
5. Repeat or terminate. The result feeds back into step 1. The loop continues until the model decides the task is complete, a step limit is hit, or an error requires escalation.

A simplified version of that loop looks like this in pseudocode:

state = {"goal": user_goal, "history": []}

while not done and steps < max_steps:
    action = model.decide_next_action(state)
    if action.type == "respond":
        return action.content
    result = execute_tool(action.tool, action.arguments)
    state["history"].append({"action": action, "result": result})
    steps += 1

The simplicity of this loop is deceptive. Almost all of the hard engineering work - and almost all of the failure modes - live inside decide_next_action and execute_tool.

Tool Use: How Agents Take Action

Tool use (sometimes called "function calling") is what turns a language model from a text generator into something that can affect the world. The mechanism is straightforward: the model is given a list of available tools, each with a name, a description, and a schema for its arguments. When the model decides a tool is needed, it outputs a structured call - typically JSON - matching that schema, and the surrounding application executes it.

The quality of an agent depends heavily on the quality of its tool definitions. A tool description that's vague or overlapping with another tool will cause the model to pick the wrong one, or hesitate between them. A well-designed tool set usually has:

• Clear, non-overlapping responsibilities per tool.
• Descriptions that state not just what the tool does, but when to use it.
• Tight, well-typed argument schemas rather than a single freeform "input" string.
• Predictable, structured return values the model can reason about on the next turn.

This is also where protocols like MCP come in - they standardize how tools are described and exposed to a model, so the same tool server can be reused across different agents and applications instead of being re-implemented for each one.

Memory: What Agents Actually Retain

"Memory" in agent systems is an overloaded term that usually refers to three distinct things:

• Working memory - the conversation and tool-call history within the current task, passed back to the model on every turn. This is the primary mechanism agents use to avoid repeating themselves mid-task.
• Long-term memory - information persisted across sessions, typically stored outside the model in a database or vector store and retrieved when relevant. This lets an agent recall facts from a previous conversation days later.
• Environment state - the actual state of the systems the agent is operating on (files on disk, rows in a database, the contents of a ticket). This isn't "memory" the model holds, but state it can re-query at any time - and often the more reliable source of truth than anything cached in the conversation.

A common design mistake is conflating these. Stuffing everything into the working-memory context window degrades performance as the context grows and gets expensive fast. The more robust pattern is to keep working memory lean - a summary of what's been done, not a full transcript - and let the agent re-fetch environment state on demand rather than trusting a stale copy from three steps ago.

Planning: Deciding What To Do Next

Planning is where agents most visibly succeed or fail. Two broad approaches dominate current systems:

Reactive planning has the model decide one step at a time, re-evaluating after every tool result. This is simpler to implement and self-corrects quickly when a tool call returns something unexpected, but it can wander on tasks with many steps because the model never commits to an overall strategy.

Upfront planning has the model produce a multi-step plan before executing anything, then work through it - optionally re-planning if a step fails. This tends to produce more coherent execution on complex, well-scoped tasks, but is more brittle when the environment doesn't match the model's assumptions.

Most production agents use a hybrid: a loose upfront plan for structure, combined with reactive re-evaluation after each tool call so the agent can adapt without discarding the whole plan every time something unexpected happens.

Common Mistakes

• Giving the agent too many tools. A model choosing between thirty overlapping tools makes worse decisions than one choosing between five well-scoped ones. Group and prune aggressively.
• No step limit or termination condition. Without a hard ceiling on steps and a clear definition of "done," agents can loop indefinitely on tasks they can't complete, burning time and cost.
• Trusting stale context over live state. Agents that act on a cached view of the world (a file's contents from three steps ago, a database row that's since changed) produce actions that don't match reality.
• No human checkpoint for irreversible actions. Sending an email, deleting a file, or committing a payment should generally pause for confirmation rather than being fully autonomous, especially early in an agent's deployment.
• Treating the system prompt as the only place reliability lives. Reliability comes from the loop's structure - validation, retries, step limits, tool design - not from clever prompt wording alone.

Practical Checklist

• Define a small, non-overlapping set of tools with clear descriptions and typed arguments.
• Set an explicit step limit and a clear termination condition for the task.
• Keep working memory to a running summary, not a full raw transcript.
• Re-fetch environment state rather than relying on cached results for anything that might have changed.
• Require explicit confirmation before irreversible actions.
• Log every tool call and result so failures are debuggable after the fact.

Conclusion

An AI agent is, at its core, an LLM wrapped in a loop with the ability to call tools, retain state, and decide when it's finished. The model provides the reasoning; the surrounding system - tool design, memory management, planning strategy, and guardrails - determines whether that reasoning turns into reliable work. Understanding this separation is the first step toward building an agent that does something useful rather than one that just looks impressive in a demo. The next step is building one - which is where the practical implementation details, and the mistakes that only show up once real tools and real data are involved, come in.