How an AI Agent Works: Runtime, Tools, and Memory

Updated 5 November 2026 · Standard formula, shown below

An AI agent works by repeatedly sending a task and its current context to a model, then acting on the model’s response. The runtime manages that loop, runs approved tools when requested, and returns results to the model until it can respond or must stop. To follow the process, you need only a basic understanding of prompts, model responses, and software functions.

What You’ll Need

A model that can accept messages and return either a response or a structured tool request.
A runtime: the application code that calls the model, manages conversation state, dispatches tools, and decides when to stop.
At least one optional tool, such as a calculator or a read-only search function, with a clear input and output format.
A place to store state if the agent must retain information between separate runs. For a simple single task, in-memory state may be enough.

Step-by-Step

Define the task and boundaries. State what the agent should accomplish, what it must not do, and what counts as completion. For example, “Find the total of these supplied figures and show the calculation” is more testable than “Help with my finances.” Decide which actions need user confirmation and which data the agent is allowed to access.
Prepare the initial context. The runtime assembles messages for the model. These commonly include system instructions, the user’s request, relevant prior conversation, and descriptions of available tools. Keep instructions and data distinct, and include only information needed for the task. A model does not automatically know the state of an external application unless the runtime provides it.
Send the context to the model. The runtime makes a model call and receives a response. The response might be ordinary text, a request to call a named tool with arguments, or a signal that the model needs more information. Treat model output as a proposed next step, not as an action that has already happened.
Check for a tool request. If the model requests a tool, the runtime verifies that the tool exists, the arguments match its expected schema, and the request is permitted. Reject unknown tools and malformed inputs. Apply extra checks to actions that change data, send messages, spend money, or affect other people; ask for confirmation when appropriate.
Run the tool in the application. The model usually does not execute the tool itself. Your runtime calls the actual function or service, ideally with limited permissions and sensible timeouts. A tool might return a result, an error, or a request for missing information. Do not expose credentials or unrestricted system access through a tool.
Return the result to the model. Add the tool’s result to the conversation in the format the model expects, associated with the tool call it answers. Then call the model again. This is the central loop: model request, runtime validation, tool execution, tool result, and another model response. A tool result is evidence supplied to the model; the model may need to interpret it or call another tool.
Repeat with limits. Continue while the model requests allowed tools and the task is not complete. Set a maximum number of turns, a time limit, and appropriate token or cost limits. Stop on completion, a refusal, an unrecoverable tool error, a limit, or a need for user input. Do not let an agent retry a failing action indefinitely.
Return a clear final answer. The runtime should distinguish a final response from another tool request. Present the answer with relevant caveats, and report uncertainty or failure rather than claiming a tool succeeded when it did not. If the task needs approval or information, ask for it explicitly.
Choose what to keep as memory. Conversation history is short-term context: it lets the model refer to earlier turns in the current task. Longer-term memory is information saved for later runs, such as a user preference or a compact summary. Store only useful, permitted information, identify its source and age where relevant, and provide a way to review or delete it. Retrieve only relevant memory and include it in a later model call; storage alone does not make the model remember it.
Test the whole loop. Try a normal task, a request with missing details, invalid tool arguments, a tool timeout, and an action that should require confirmation. Check logs for model requests, tool inputs and outputs, stop reasons, and errors. Redact secrets and unnecessary personal data. Test that the agent stops safely when a tool fails or the turn limit is reached.

Common Mistakes

Assuming the model runs tools. Usually the runtime must execute each requested function and return its result.
Trusting every tool call. Validate names, arguments, permissions, and side effects before dispatching.
Confusing context with memory. A fact in one request is not necessarily retained in a later session. Persistent memory requires explicit storage and retrieval.
Saving entire conversations by default. This can preserve irrelevant or sensitive details. Prefer a minimal, useful summary and respect retention requirements.
Skipping stop conditions. Without limits, the agent may loop, consume resources, or repeat actions. Use turn caps, timeouts, and safeguards against duplicate side effects.
Treating a tool’s output as unquestionably correct. Tools can fail or return stale, incomplete, or unexpected data. Check errors and communicate uncertainty.

Tips

Begin with one low-risk tool and a narrow task; add capabilities only when a test shows they are needed.
Give each tool a precise description, typed inputs, predictable outputs, and a clearly stated permission boundary.
Make operations that change state idempotent where possible, or check whether they already succeeded before retrying.
Separate the model’s decision from the runtime’s enforcement. The model can suggest an action; application code should decide whether it is allowed.
Keep logs useful for debugging but avoid recording secrets, authentication tokens, or unrelated personal information.

Troubleshooting

The agent never calls a tool: Check that the tool is included in the model request, its description fits the task, and the runtime handles the model’s tool-call response. The model may also decide it has enough information to answer directly.

A tool call fails validation: Compare the requested name and arguments with the tool schema. Return a clear, bounded error to the model so it can correct the request or ask the user, rather than silently changing inputs.

The agent repeats calls or never finishes: Inspect each turn to find whether the tool result is being attached correctly and whether completion is recognized. Add a turn limit, timeout, and duplicate-action check; end the run with a useful explanation when a limit is reached.

The answer ignores a tool result: Confirm the result was returned to the model in the expected message format and linked to the right call. Make errors and important fields explicit, and avoid returning excessively large output that obscures the useful information.

Memory is missing or outdated: Verify that the application saves the record and retrieves it for the next run. Include timestamps or expiration rules when facts can change, and let the agent ask the user rather than rely on stale information.

Frequently Asked Questions

Is an AI agent just a model?
No. The model generates responses and may request actions; the runtime supplies context, executes tools, manages state, and enforces limits.

Does the model call a tool directly?
Usually it returns a structured request. The runtime validates that request, calls the function or service, and sends the result back.

What is the difference between conversation history and memory?
History is context passed along during a conversation. Memory is information deliberately saved and retrieved for future tasks.

Does an agent need memory to work?
No. Many tasks work with only the current request and tool results. Persistent memory is useful when relevant details must carry across separate runs.

How can I prevent an agent from taking an unsafe action?
Limit tool permissions, validate every request in the runtime, require confirmation for consequential actions, and set clear stopping limits.

Why can an agent give a wrong answer even after using a tool?
The tool may have returned incomplete or incorrect data, or the model may have misread it. Validate important results and show uncertainty when the evidence is insufficient.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.