How AI Agents Work: Architecture and Mechanics
In This Guide
What Makes an AI Agent Different
A large language model on its own is a text completion engine. You give it a prompt, it generates a response, and the interaction is over. An AI agent wraps that language model in a system that gives it the ability to act on the world, remember past interactions, and pursue goals across multiple steps without waiting for human input at every turn. The distinction is not about intelligence. It is about autonomy and capability.
Three properties separate an AI agent from a standard chatbot or language model API call. First, agents have tool access, meaning they can call external APIs, read databases, write files, browse the web, or execute code rather than just generating text. Second, agents have a reasoning loop that lets them observe the results of their actions and decide what to do next, rather than producing a single response and stopping. Third, agents maintain state across steps, tracking what they have done, what they have learned, and what remains to be accomplished.
These three properties work together to create systems that can handle tasks too complex for a single prompt. When you ask a chatbot to find the cheapest flight from New York to London next Tuesday, it can only describe how you might search for one. When you ask an agent with access to flight search APIs, it actually searches, compares prices, filters results, and presents the answer. The agent does real work, not just text generation.
The shift from chatbot to agent also changes the failure modes. A chatbot can produce incorrect text, but the damage is limited because a human reads the output before acting on it. An agent can take real actions with real consequences: sending emails, modifying databases, spending money on API calls, or deploying code. This is why agent architecture matters so much. The internal mechanics that govern how an agent reasons, validates, and recovers from errors determine whether the system is useful or dangerous.
The Core Components of an AI Agent
Every AI agent, regardless of framework or implementation language, contains the same fundamental components. Understanding these components and how they interact is the foundation for understanding how agents work.
The language model serves as the cognitive core of the agent. It interprets instructions, reasons about problems, generates plans, produces tool calls, and evaluates results. The model does not think in the way humans do, but it processes context (the accumulated text of the conversation, tool results, and system instructions) and generates the next token sequence that is most likely to be useful given that context. The quality of the model directly affects the quality of the agent reasoning, planning, and decision-making. Larger models with more parameters generally produce better reasoning but cost more per call and respond more slowly.
The system prompt defines the agent identity, capabilities, constraints, and behavioral guidelines. It tells the model what role it is playing, what tools are available, what rules it must follow, and how it should format its responses. A well-designed system prompt is the difference between an agent that reliably follows procedures and one that improvises unpredictably. The prompt is not just instructions. It is the agent operating manual, and it runs on every single turn of the reasoning loop.
The tool layer connects the agent to external capabilities. Tools are functions that the agent can invoke by generating a structured request (typically JSON) specifying the tool name and parameters. The agent runtime intercepts this request, executes the corresponding function, and returns the result to the agent as new context. Common tools include web search, code execution, database queries, file operations, API calls, and browser automation. The tool layer is what transforms a language model from a text generator into a system that can interact with the real world.
The memory system gives the agent the ability to store and retrieve information beyond what fits in a single context window. Short-term memory is the conversation itself, the running list of messages, tool calls, and results that the model sees on every turn. Long-term memory uses external storage like vector databases, key-value stores, or files to persist information across sessions. Episodic memory records specific past interactions so the agent can learn from its own experience. Without memory, every agent interaction starts from scratch, which makes multi-session tasks impossible.
The orchestration runtime manages the overall execution of the agent. It runs the reasoning loop, dispatches tool calls, manages context window limits, handles errors, enforces timeouts and budgets, and coordinates communication between components. The runtime is the invisible infrastructure that keeps the agent running. Most agent frameworks provide the runtime so developers can focus on defining agent behavior rather than building execution infrastructure.
The Reasoning Loop
The reasoning loop is the engine that drives every AI agent. It is the cycle of observing, thinking, acting, and evaluating that allows the agent to make progress on a task across multiple steps. Different frameworks implement the loop differently, but the core pattern is the same.
The most widely used reasoning pattern is ReAct (Reasoning and Acting), which alternates between generating reasoning text and taking actions. On each turn, the agent first reasons about the current situation: what is the goal, what has been accomplished so far, what information is missing, and what action would make the most progress. Then it selects and executes an action, typically a tool call. The result of the action is added to the context, and the cycle repeats.
A single turn of the ReAct loop looks like this: the model receives the full context (system prompt, conversation history, previous tool results), generates a reasoning step explaining its current thinking, produces a tool call to take the next action, the runtime executes the tool and returns the result, and the model processes the result to decide if the task is complete or if another action is needed. This cycle continues until the agent determines it has achieved its goal, hits a configured limit on turns, or encounters an unrecoverable error.
Planning capability separates basic agents from sophisticated ones. A basic agent makes one decision at a time, choosing the immediate next action without considering future steps. A planning agent generates a multi-step plan before acting, then executes the plan step by step, revising it when circumstances change. The plan-and-execute pattern creates a plan first, then hands each step to an executor. The advantage is better task decomposition and the ability to parallelize independent steps. The disadvantage is that the initial plan may be wrong, and rigid adherence to a bad plan wastes time and resources.
Dynamic replanning addresses the rigidity problem by allowing the agent to revise its plan based on new information gathered during execution. If the agent planned to retrieve data from an API but discovers the API is down, it can replan to use an alternative data source. If a search returns unexpected results that suggest a different approach, the agent can adjust its strategy mid-execution. This adaptability is one of the key advantages agents have over traditional automation, which follows fixed scripts that cannot respond to unexpected situations.
The depth of reasoning on each turn depends on the model and configuration. Some agents use extended thinking or chain-of-thought reasoning to work through complex problems step by step before choosing an action. Others use a faster, more direct style for routine tasks. The tradeoff is between accuracy and speed. Complex tasks that require careful analysis benefit from extended reasoning. Simple, repetitive tasks are better served by fast execution with minimal deliberation.
How Agents Use Tools
Tool use is what gives agents the ability to affect the real world. Without tools, a language model can only generate text about what it would do. With tools, it actually does it. The mechanics of tool use involve several steps that happen on every tool call.
First, the agent decides which tool to use and what parameters to pass. This decision is made by the language model based on the current context and the tool descriptions provided in the system prompt. Each tool has a name, a description of what it does, and a schema defining the expected parameters. The model generates a structured tool call that specifies the tool name and parameter values. Modern models from Anthropic, OpenAI, and Google have been specifically trained to produce well-formed tool calls, which dramatically reduces the error rate compared to earlier approaches that relied on the model generating arbitrary JSON.
Second, the agent runtime validates the tool call. It checks that the requested tool exists, that the parameters match the expected schema, and that the agent has permission to use this tool. Validation catches many errors before they reach the external service, which prevents wasted API calls and reduces the chance of unintended side effects.
Third, the runtime executes the tool by calling the corresponding function with the provided parameters. The function might make an HTTP request to an external API, run a database query, execute a shell command, read a file from disk, or perform any other operation. The execution happens outside the language model. The model does not run code itself. It generates the instruction, and the runtime carries it out.
Fourth, the tool returns its result to the agent. The result is typically text or structured data that gets added to the conversation context. The agent then processes this result on its next reasoning turn, using it to decide what to do next. If the tool returned the expected data, the agent might proceed to the next step. If the tool returned an error, the agent might retry with different parameters, try an alternative approach, or report the failure.
The number and variety of tools available to an agent determine its practical capabilities. An agent with access to web search, code execution, file operations, and database queries can handle most knowledge work tasks. An agent limited to web search alone can only research and report. Tool design is as important as model selection because the quality of the tools directly affects the quality of the agent work.
Context Windows and Memory
The context window is the total amount of text a language model can process on a single turn. Every message in the conversation, every system instruction, every tool description, and every tool result occupies space in the context window. When the context window fills up, older information must be removed or summarized to make room for new information. This constraint fundamentally shapes how agents work.
Context window sizes have grown dramatically. Early GPT-3 models had 4,096 tokens (roughly 3,000 words). Current models from Anthropic and Google support 200,000 tokens or more, enough to process entire books in a single context. Despite these larger windows, context management remains critical because every token in the context costs money, adds latency, and can degrade the model attention to the most relevant information. A model with a 200,000 token window does not necessarily reason well about all 200,000 tokens equally. Information at the beginning and end of the context tends to receive more attention than information in the middle.
Agents manage context through several strategies. Sliding window approaches keep only the most recent N messages and discard older ones. Summarization compresses older conversation history into shorter summaries that preserve the key facts while reducing token count. Selective inclusion loads only the context relevant to the current step, using embeddings or keyword matching to find the most pertinent past interactions. Hierarchical memory stores information at different levels of detail, with the agent retrieving the appropriate level based on its current needs.
Long-term memory extends the agent knowledge beyond a single conversation. Vector databases store information as numerical embeddings that can be searched by semantic similarity. When the agent needs information from a past interaction, it queries the vector database with the current question and retrieves the most relevant stored information. This approach lets agents access millions of stored facts without loading them all into the context window. The tradeoff is retrieval accuracy, since the vector search might not find the most relevant information, or might return information that seems relevant but is actually from a different context.
Working memory tracks the agent current state within a task: what steps have been completed, what intermediate results have been gathered, what decisions have been made, and what remains to be done. Working memory is typically stored in a structured format (JSON, key-value pairs, or a task graph) rather than as natural language, which makes it more reliable for the agent to read and update. Losing working memory mid-task forces the agent to reconstruct its progress from the conversation history, which is slow, error-prone, and expensive.
Workflows and Execution Patterns
AI agents execute work through different workflow patterns depending on the complexity and structure of the task. The workflow pattern determines how many steps the agent takes, whether those steps happen sequentially or in parallel, and how much autonomy the agent has in deciding the next action.
Single-step workflows handle tasks that require just one action. Classify this email, summarize this document, extract the key dates from this contract. The agent receives the input, processes it in one reasoning turn, and returns the result. Single-step workflows are fast, cheap, and predictable. They are the right choice when the task is well-defined and the required information is already in the prompt.
Linear multi-step workflows handle tasks that require a fixed sequence of actions. Research a topic, then write a report, then format it, then save it. Each step depends on the output of the previous step, so the steps execute in order. Linear workflows are easy to design, debug, and monitor because the execution path is predictable. The limitation is that they cannot adapt to unexpected results.
Branching workflows allow the agent to choose different paths based on intermediate results. Branching workflows handle real-world variability better than linear workflows because they can adapt to what they find. The cost is increased complexity in design and testing, since every branch creates a new execution path that needs to work correctly.
Autonomous workflows give the agent maximum freedom to decide its own sequence of actions. The agent receives a goal (not a fixed procedure), reasons about how to achieve it, takes actions, evaluates progress, and continues until the goal is met. This is the most powerful pattern because it handles novel situations that the designer could not anticipate. It is also the most risky because the agent might take unexpected actions, enter loops, or pursue inefficient strategies. Autonomous workflows require strong guardrails: turn limits, cost budgets, action approval gates, and monitoring.
Parallel workflows split independent subtasks across multiple execution threads or agents. Parallel execution reduces total completion time proportionally to the number of parallel tasks. The complexity lies in coordinating the results, handling partial failures, and managing the aggregate resource consumption.
State Management
State management determines what the agent knows about its current situation and how that knowledge persists across steps, sessions, and failures. Every agent maintains state, whether implicitly through the conversation history or explicitly through dedicated state stores. How that state is managed affects reliability, resumability, and cost.
The simplest form of state is the conversation history itself. Each message, tool call, and tool result is appended to the conversation, and the model receives the entire conversation on every turn. This approach works well for short tasks but breaks down for long-running operations because the context window fills up, costs increase with every turn, and the model ability to attend to relevant information degrades as the context grows.
Explicit state management separates the agent working state from the conversation history. A state object tracks the current step in the workflow, intermediate results, pending actions, error counts, and any other information the agent needs to continue its work. This state object is compact and structured, making it cheap to include in every turn and easy for the model to interpret.
Persistent state survives agent restarts and crashes. If an agent fails halfway through a ten-step task, persistent state lets it resume from step six rather than starting over from step one. The state is written to a database, a file, or a key-value store after each step, creating a checkpoint that the agent can restore from.
Distributed state enables multiple agents to share information and coordinate their work. When a supervisor agent assigns subtasks to worker agents, the workers need to report their progress and results back through a shared state mechanism. The challenge is consistency, since multiple agents updating the same state simultaneously can create conflicts that produce incorrect results.
Error Handling and Self-Correction
Errors are inevitable in agent systems. APIs return unexpected responses. Models generate malformed tool calls. External services go down. Data is missing or formatted incorrectly. The quality of an agent system depends less on whether errors occur and more on how the system handles them when they do.
The first line of defense is validation. Before executing any tool call, the runtime checks that the parameters are valid, the tool exists, and the request makes sense. Catching errors before execution prevents wasted API calls and avoids side effects from malformed operations.
When errors occur during execution, the agent has several recovery strategies. Retry with the same parameters handles transient failures like network timeouts or rate limiting. Retry with modified parameters handles errors caused by incorrect inputs. The agent examines the error message, reasons about what went wrong, and generates a corrected tool call. This self-correction capability is one of the most powerful features of LLM-based agents, because the model can often diagnose the problem and fix it without human intervention.
Fallback strategies provide alternative paths when the primary approach fails. If the primary API is down, try a secondary API. If a specific search query returns no results, broaden the search terms. Well-designed agents have fallback strategies for every critical operation, ensuring that a single point of failure does not halt the entire task.
Escalation to human operators handles errors that the agent cannot resolve on its own. When the agent encounters a situation it does not understand, when it has exhausted all automated recovery strategies, or when the error involves a decision that requires human judgment, the agent pauses execution and requests human intervention.
Human-in-the-Loop Patterns
Not every task should be fully autonomous. Human-in-the-loop patterns define where and how humans interact with agent execution, balancing the efficiency of automation with the judgment and oversight that humans provide.
Approval gates pause the agent before executing high-impact actions. An agent managing email outreach might generate draft emails autonomously but require human approval before sending them. An agent modifying production databases might prepare the changes but wait for a human to confirm before applying them. Approval gates are configurable, so the same agent might run autonomously for low-risk operations and pause for approval on high-risk ones.
Review checkpoints let humans inspect the agent work at defined intervals without blocking execution. After every five steps, or at the end of each phase, the agent presents a summary of what it has done and what it plans to do next. The human can approve, modify, or redirect the agent approach.
Collaborative editing combines human and agent capabilities on the same task. The agent generates a first draft, the human edits it, the agent processes the edits and generates a refined version, and the cycle continues until the result meets the human standards. This pattern leverages the agent speed and the human judgment, producing better results than either could achieve alone.
Model Selection and Routing
Sophisticated agent systems use different models for different tasks within the same workflow. A small, fast model handles routine decisions like classifying inputs or extracting structured data. A large, capable model handles complex reasoning, planning, and generation. The routing logic decides which model to use for each step based on the task complexity, required accuracy, latency constraints, and cost budget.
Model routing reduces costs dramatically. A typical agent workflow might include dozens of turns, but only a few of those turns require the full reasoning power of a frontier model. Routing simple turns to smaller models can reduce the total cost of a task by 50 to 80 percent without meaningfully affecting quality. The key is identifying which turns are simple and which require advanced reasoning, and setting up the routing rules accordingly.
Some agent systems also select models based on the type of output needed. Code generation tasks might route to models specifically trained on code. Multilingual tasks route to models with strong performance in the target language. Structured data extraction routes to models with reliable JSON output. This specialization improves quality because each model operates in its area of strength.