Introduction

“AI agents” are everywhere in current discourse: customer support agents, coding agents, research agents, planning agents. But the term is often used loosely, sometimes referring to:

  • A single large language model (LLM) call
  • A script that calls a model and then an API
  • A complex system that plans, acts, remembers, and adapts over time

To design, evaluate, or improve AI agents, you need a clear mental model of what an agent actually is and how its parts work together.

This article breaks down the detailed anatomy of an AI agent: the core components, how they interact, common architectural patterns, and where current technologies actually stand. We’ll focus on LLM-centered agents (since they dominate today’s tooling) but most concepts generalize to other agent types (robotics, game AI, classical RL).

1. From Models to Agents

1.1 Model vs. Agent

A model is a function:

Inputs → Model → Outputs

For example, “predict the next token in a sequence” or “classify this image.”

An agent wraps one or more models in a loop that can:

  • Observe the environment
  • Decide what to do
  • Take actions
  • Update its internal state (e.g., memory)
  • Repeat over time

Formally, an agent implements an agent-environment loop:

State_t, Observation_t → Agent → Action_t, Internal State_{t+1}

The key differences:

  • Temporal continuity: agents act over multiple steps, not just a single prediction.
  • Goal orientation: agents pursue objectives (explicit or implicit).
  • Interaction: agents affect their environment (APIs, files, robots, etc.) and respond to changes.

1.2 A High-Level Mental Model

At a high level, an AI agent typically consists of:

  1. Perception: understanding inputs from the environment.
  2. Memory: storing and retrieving relevant information over time.
  3. Reasoning & Planning: deciding what to do next and how.
  4. Action & Tools: executing steps in the external world.
  5. Control Logic (Policy): orchestrating all of the above in a loop.
  6. Objectives & Constraints: defining what “good behavior” means.

We’ll now unpack each of these in more detail.

2. The Core Agent Architecture

2.1 The Agent Loop

Most AI agents can be described with a loop like this:

  1. Observe the environment.
  2. Update internal state (e.g., memory).
  3. Decide what action to take (reasoning/planning).
  4. Act through tools or APIs.
  5. Evaluate the result (success/failure, progress).
  6. Repeat until a stopping condition is met.

In (simplified) Python-like pseudocode:

class Agent:
    def __init__(self, policy, memory, tools, state=None):
        self.policy = policy          # decision-making component
        self.memory = memory          # short- and long-term memory
        self.tools = {t.name: t for t in tools}  # tools/actions
        self.state = state or {}      # agent-specific state

    def step(self, observation):
        # 1. Update working memory with the latest observation
        self.memory.store_observation(observation)

        # 2. Build context (relevant history, docs, etc.)
        context = self.memory.build_context(observation)

        # 3. Ask the policy what to do next
        decision = self.policy.decide(
            observation=observation,
            context=context,
            tools=list(self.tools.values()),
            state=self.state
        )

        # 4. Execute actions (tool calls or replies)
        result = self.execute(decision)

        # 5. Update memory with the results of actions
        self.memory.store_result(result)

        # 6. Check stopping condition
        done = decision.done or self._goal_reached(result)

        return result, done

    def execute(self, decision):
        # Simplified: handle one action at a time
        if decision.action_type == "tool_call":
            tool = self.tools[decision.tool_name]
            output = tool.run(**decision.tool_args)
            return {"type": "tool_result", "output": output}
        elif decision.action_type == "respond":
            return {"type": "response", "message": decision.message}
        else:
            return {"type": "noop"}

Everything else—perception, planning, memory, tool use—plugs into this loop.

3. Perception & Environment Interface

3.1 What Counts as “Perception”?

In classical AI, perception is processing raw sensory data (images, audio, proprioception). In today’s LLM-based agents, perception often means:

  • Parsing textual input (user queries, documents, logs)
  • Decoding structured data (JSON, API responses, database rows)
  • Optionally processing multimodal inputs (images, code, tables)

The key goal: transform raw input into an internal representation the agent’s policy and reasoning modules can understand.

3.2 The Environment Interface Layer

The environment interface defines everything the agent can see and potentially act on. This might include:

  • User messages (natural language)
  • Files or documents
  • Data sources (databases, APIs, logs)
  • External systems (ticketing systems, CRMs, CI/CD pipelines)
  • Physical sensors (for robots)

A typical environment interface pipeline:

  1. Input adapters

    • Convert external formats to canonical internal structures.
    • E.g., HTTP → internal Observation object.

  2. Parsing & validation

    • Schema validation, error handling, normalization.
    • E.g., convert date strings to datetime, handle missing fields.

  3. Feature extraction / embedding (optional)

    • Generate embeddings for retrieval.
    • Extract key entities, intents, or labels.

Example of an observation structure:

from dataclasses import dataclass
from typing import Dict, Any, Optional

@dataclass
class Observation:
    source: str                   # "user", "api", "file", "sensor"
    content: str                  # text representation
    metadata: Dict[str, Any]      # timestamps, ids, etc.
    raw: Optional[Any] = None     # original data if needed

The agent logic works with this normalized structure rather than vendor-specific or raw formats.

4. Memory: Short-Term, Long-Term, and Beyond

Memory is core to “agent-ness.” Without it, your system is just a stateless function.

4.1 Types of Memory

  1. Working (Short-Term) Memory

    • Active context for the current episode: recent messages, steps taken, intermediate results.
    • Typically fits into the LLM context window.
    • Implemented as: message buffers, scratchpads, temporary variables.

  2. Long-Term Memory

    • Information that persists across episodes or long tasks.
    • Implemented with:
      • Vector databases (FAISS, Chroma, pgvector, etc.)
      • Databases (SQL/NoSQL)
      • Object stores (S3, file systems)

  3. Episodic vs. Semantic vs. Procedural

    • Episodic: “What happened when?”
      Logs of conversations, actions, failures, successes.

    • Semantic: “Facts about the world.”
      Knowledge bases, documentation, FAQs.

    • Procedural: “How to do things.”
      Recipes, workflows, scripts, reusable plans.

A robust agent architecture often maintains all three, though not always explicitly labeled as such.

4.2 Memory Operations

Useful memory modules expose four main functions:

  1. Store: write an item to memory
  2. Retrieve: find relevant items given a query or context
  3. Update: modify existing entries as things change
  4. Summarize / Compress: periodically condense detailed logs into shorter summaries

Example sketch:

class Memory:
    def __init__(self, vector_store, summary_store):
        self.vector_store = vector_store    # for semantic/episodic details
        self.summary_store = summary_store  # for compressed history

    def store_observation(self, obs: Observation):
        text = self._serialize(obs)
        embedding = embed(text)
        self.vector_store.add(text=text, embedding=embedding, metadata=obs.metadata)

    def store_result(self, result):
        text = self._serialize(result)
        embedding = embed(text)
        self.vector_store.add(text=text, embedding=embedding,
                              metadata={"type": result["type"]})

    def build_context(self, current_obs, k=10):
        query = current_obs.content
        results = self.vector_store.search(query=query, top_k=k)
        summaries = self.summary_store.get_recent()
        return {
            "current": current_obs,
            "retrieved": results,
            "summaries": summaries
        }

    def summarize_if_needed(self):
        # Periodically compress detailed history into summaries
        # to fit within budget constraints.
        pass

4.3 Memory in Practice (and Its Limits)

In real-world LLM agents:

  • “Memory” often means just “chat history in the prompt” plus a vector search over documents.
  • Persistence across sessions is usually opt-in (for privacy and safety).
  • Automatic, continuous learning across users is rare due to risk of:
    • Privacy leaks
    • Catastrophic forgetting or corruption
    • Unintended behavior changes

When designing an agent, be explicit about:

  • What should persist vs. what must remain ephemeral
  • Who owns and can inspect the stored memory
  • Retention and deletion policies

5. Reasoning, Planning, and Decision-Making

At the heart of an agent is a policy: a mapping from observations and internal state to actions.

5.1 Policy: The Decision-Making Core

In LLM-based agents, the policy is often an LLM call guided by instructions:

  • System prompt: identity, role, constraints
  • Tool specification: what tools are available and how to call them
  • Context: memory, retrieved documents, current observation
  • Output schema: how to format actions, intermediate thoughts, and final responses

Conceptually:

class LLMPolicy:
    def __init__(self, llm, system_prompt, output_schema):
        self.llm = llm
        self.system_prompt = system_prompt
        self.output_schema = output_schema  # e.g., Pydantic model, JSON schema

    def decide(self, observation, context, tools, state):
        prompt = self._build_prompt(
            observation=observation,
            context=context,
            tools=tools,
            state=state
        )
        raw_output = self.llm.generate(prompt, schema=self.output_schema)
        decision = self._parse_decision(raw_output)
        return decision

5.2 Reasoning Styles

Common reasoning paradigms in modern agents:

  1. Direct response (no explicit reasoning)

    • One-shot or few-shot answers.
    • Lowest latency, poorest reliability on complex tasks.

  2. Chain-of-Thought (CoT)

    • Model generates intermediate reasoning steps.
    • Can be explicit (visible) or hidden from the user.
    • Improves reliability on multi-step problems but increases token usage.

  3. ReAct (Reason + Act)

    • Interleaves thinking and tool use.
    • Loop: Think → Act (tool call) → Observe → Think → …
    • Especially useful for tool-using agents and problem-solving.

  4. Planning + Execution

    • First generate a plan (sequence of steps).
    • Then execute step-by-step, updating the plan as needed.
    • Can combine with tools and CoT for robust performance.

5.3 Planning Subsystem

For non-trivial tasks, agents benefit from explicit planning:

  • Task decomposition: break a big goal into manageable subtasks.
  • Ordering & dependencies: determine what must happen first.
  • Resource estimation: approximate cost, time, or API usage.
  • Monitoring: track progress vs. plan, detect when replanning is needed.

Example of a planning-oriented prompt pattern (simplified):

You are a planning agent. Given a user goal and context,
output a numbered list of high-level steps that an execution
agent can follow. Each step should be:

- Specific
- Feasible
- Testable (has a clear completion criterion)

The output becomes a procedural memory or a task graph the execution agent follows.

5.4 Policies Beyond LLMs

While today’s popular agents rely heavily on LLMs, the policy could also be:

  • A reinforcement learning (RL) policy network
  • A rule-based or hybrid system
  • A search procedure (MCTS, A*, etc.)
  • A learned controller over a library of skills

The core idea is the same: decide what to do next, given what you know and what you want.

6. Actions, Tools, and Actuators

6.1 What Are “Tools”?

In modern LLM agents, tools are functions the model can ask the system to execute. They can represent:

  • API calls (e.g., “search_documents”, “fetch_issue”, “send_email”)
  • System operations (read/write files, run shell commands)
  • Domain-specific operations (place orders, generate reports)
  • Other models (embedding models, image generators, code executors)

Tools turn a passive model into an active agent capable of affecting the world.

6.2 Tool Interface Design

Each tool usually has:

  • A name
  • A description (so the LLM knows when to use it)
  • A schema for inputs and outputs
  • An implementation (the actual code)

Example:

from typing import TypedDict, List

class SearchInput(TypedDict):
    query: str
    top_k: int

class SearchResult(TypedDict):
    document_id: str
    snippet: str

class SearchTool:
    name = "search_documents"
    description = "Searches internal knowledge base for relevant documents."

    input_schema = SearchInput
    output_schema = List[SearchResult]

    def run(self, query: str, top_k: int = 5) -> List[SearchResult]:
        # Implementation detail omitted
        ...

The LLM receives a description like:

{
  "name": "search_documents",
  "description": "Searches internal knowledge base for relevant documents.",
  "parameters": {
    "type": "object",
    "properties": {
      "query": { "type": "string" },
      "top_k": { "type": "integer", "default": 5 }
    },
    "required": ["query"]
  }
}

and can then decide to call it with structured arguments.

6.3 Tool Use Loop

A typical tool-using agent cycle:

  1. Model is given:
    • Current conversation
    • Available tools & schemas
    • Instructions for how to call tools
  2. Model outputs either:
    • A direct response, or
    • A tool invocation (name + arguments)
  3. The system:
    • Validates and executes the tool
    • Captures the result
  4. The result is fed back to the model as input for the next step

This is where the “ReAct” pattern fits naturally:

“Thought: I should look up more information.
Action: search_documents({query: "..."})
Observation: [tool result]
Thought: Now I know the answer.
Action: respond to the user.”

6.4 Safety and Guardrails for Tools

Because tools can alter the real world, you need guardrails:

  • Authorization layer

    • Which users / contexts can trigger which tools?
    • E.g., viewing vs. modifying production data.

  • Constraints

    • Rate limits, budget limits, safe parameter ranges.

  • Human-in-the-loop checks

    • Require confirmation for dangerous actions (deleting data, financial transactions).

  • Logging & auditing

    • Maintain traces of tool calls and rationale for debugging and compliance.

7. World Models and Internal Representations

Beyond immediate observations and memory, many agents implicitly or explicitly maintain a world model:

A structured representation of how the environment works and how actions change it.

This might include:

  • The current state of a task or workflow
  • Objects, entities, and their relations (knowledge graph)
  • Models of other agents or users (preferences, behavior)
  • Domain rules and constraints (business logic)

7.1 Explicit vs. Implicit World Models

  • Implicit:
    The LLM’s weights encode millions of patterns and relationships but they are:

    • Not directly inspectable
    • Not updated in real-time
    • Not guaranteed to match your specific domain perfectly

  • Explicit:

    • Data structures your system maintains and updates:
      • Databases
      • Knowledge graphs
      • State machines
      • Domain models (e.g., tickets, orders, deployments)

A robust agent architecture usually combines both:

  • Use the LLM for plausible reasoning and language understanding.
  • Use explicit state and rules for hard constraints and truth maintenance.

8. Objectives, Rewards, and Alignment

8.1 Goals and Success Criteria

An agent needs clear answers to:

  • What am I trying to achieve?
  • How do I know if I’m making progress?
  • When should I stop?

Goals can be:

  • User-specified (“Summarize this document.”)
  • System-specified (“Resolve customer tickets with high satisfaction.”)
  • Implicit (learned from RL or imitation learning)

Success measures might include:

  • Task completion (binary or graded)
  • User feedback / ratings
  • Business KPIs (conversion rate, resolution time)
  • Formal metrics (accuracy, latency, cost)

8.2 Rewards vs. Heuristics

In classical RL, agents maximize a formal reward function. In many modern LLM agents:

  • There is no continuous numerical reward at each step.
  • Instead, they rely on:
    • Heuristics (e.g., “stop when answer is produced and user seems satisfied”)
    • Static instructions (“be concise, be accurate, use tools when needed”)
    • Offline training (RLHF or similar methods applied during model training)

You can still add online feedback loops:

  • Ask users for ratings and:
    • Use them to tune prompts, routing, or tool choices.
    • Use them for offline fine-tuning or reward modeling.

8.3 Alignment and Constraints

Alignment mechanisms for agents include:

  • Strong system prompts that define:
    • Role (assistant, coder, planner)
    • Non-goals (no harmful actions, no policy violations)
    • Tool usage rules
  • Policy filters:
    • Check outputs for safety (PII, harmful content, etc.).
  • Action filters:
    • Validate tool parameters, block unsafe combinations.
  • Monitoring:
    • Detect anomalous behavior and dynamically restrict capabilities.

You can think of “alignment” as part of the agent’s anatomy: the constraint system that shapes what it is allowed to do in pursuit of its goals.

9. Orchestration and Control Logic

Many practical AI agents are actually systems of components:

  • Orchestrators (routers, planners)
  • Specialist sub-agents
  • Tools and services
  • Memory stores

The orchestration layer coordinates these pieces.

9.1 Single-Agent vs. Multi-Agent Orchestration

  • Single-agent:

    • One main policy that has access to all tools.
    • Simpler, less overhead, easier to debug.

  • Multi-agent:

    • Several specialized agents:
      • Planner agent
      • Research agent
      • Coding agent
      • Reviewer agent
    • Communication protocols between agents (messages, shared boards, blackboards).
    • Can increase modularity and clarity at the cost of complexity and latency.

9.2 Routing and Specialization

Common orchestration patterns:

  • Skill routing:

    • Decide whether to:
      • Answer directly
      • Call a specific sub-agent
      • Use a specific tool
    • Implemented via:
      • A small “router” LLM
      • Heuristics / rules
      • Embedding-based similarity (match queries to skills)

  • Hierarchical agents:

    • A top-level manager agent delegates to worker agents.
    • Workers focus on narrow tasks; manager integrates results.

9.3 Error Handling and Recovery

Real-world environments are messy. Agents must handle:

  • Tool failures (network errors, invalid parameters, rate limits)
  • Inconsistent or missing data
  • Partial progress and interruptions

Key patterns:

  • Retry policies (with exponential backoff, alternative tools)
  • Fallback strategies (simpler methods, human escalation)
  • Checkpointing (save intermediate state for recovery)
  • Self-repair (ask the model to diagnose and fix its own errors when possible)

10. Putting It Together: Example Architectures

To make the anatomy concrete, here are three simplified agent patterns.

10.1 Pattern 1: Stateless Helper with Tools

  • No long-term memory
  • Single LLM call per request
  • Tools allowed within that call

Use case: simple support bots, small utilities.

Flow:

  1. Build prompt with:
    • System + user message
    • Tool definitions
  2. Call LLM
  3. If tools are invoked:
    • Execute tool
    • Call LLM again with tool result
  4. Return answer

Pros: simple, predictable.
Cons: no cross-request continuity, limited context.

10.2 Pattern 2: Stateful Task Agent with Memory

  • Per-task working memory
  • Limited long-term memory
  • Multi-step reasoning & tool usage

Use case: research assistants, coding agents for a single repo, document workflows.

Flow:

  1. Initialize memory for the task.
  2. Loop until done:
    • Observe latest user or environment input.
    • Update memory.
    • Call policy → get action (think, tool, respond).
    • Execute tools, update memory.
  3. Persist task summary at the end to long-term memory.

Pros: handles multi-step tasks, can revisit context.
Cons: still task-bounded; cross-task learning usually manual.

10.3 Pattern 3: Multi-Agent Workspace

  • Planner agent
  • Specialist agents (research, coding, data analysis, etc.)
  • Shared memory / knowledge base
  • Explicit world model (e.g., project board, tickets, artifacts)

Use case: complex projects (software development, multi-document analysis, workflows).

Flow:

  1. User specifies high-level goal.
  2. Planner decomposes into tasks on a shared board.
  3. Specialist agents pick up tasks based on type.
  4. Results stored in shared memory and artifacts (files, reports).
  5. Planner monitors progress, reprioritizes, replans.
  6. A “reporting agent” generates final outputs for humans.

Pros: modular, scalable to complex workflows.
Cons: higher complexity, cost, and need for robust coordination.

11. Practical Design Considerations

11.1 Start Simple, Add Complexity Later

When designing an agent:

  1. Start with:

    • A clear goal
    • One policy (LLM)
    • 1–3 critical tools
    • Basic working memory (conversation history)

  2. Add, in this order (if needed):

    • Retrieval over documents (semantic memory)
    • Planning (explicit task decomposition)
    • Long-term episodic memory
    • Multiple specialized agents
    • Feedback-based learning

This incremental approach avoids over-engineering and makes debugging easier.

11.2 Observability and Tracing

To work effectively with agents, you need visibility into:

  • Prompts and model outputs
  • Tool invocations and parameters
  • Decisions taken at each step
  • Latencies and costs
  • Failure modes

Implement:

  • Structured logs (JSON logs with step types and IDs)
  • Traces (per-task timelines of decisions and tool calls)
  • Replay tools to debug and refine the agent’s behavior

11.3 Testing and Evaluation

Testing agents is harder than testing deterministic code, but still crucial:

  • Unit tests for tools and orchestrator code
  • Scenario tests for common workflows
  • Regression tests when upgrading models or prompts
  • Offline evaluation:
    • Benchmarks, curated examples, success/failure labels
  • Online evaluation:
    • A/B tests, user feedback, business metrics

12. Summary: The Anatomy Checklist

When you say “AI agent,” you are talking about a system with (at least) these elements:

  1. Environment Interface

    • Inputs: user messages, data, sensors
    • Normalization and parsing

  2. Perception & Representation

    • Text/multimodal understanding
    • Optional feature extraction (embeddings)

  3. Memory

    • Working memory (within the current task/session)
    • Long-term memory (episodic, semantic, procedural)
    • Retrieval, storage, summarization

  4. Policy / Controller

    • Often an LLM-based decision function
    • Takes observations + context → outputs actions

  5. Reasoning & Planning

    • CoT, ReAct, planning strategies
    • Decomposition of complex goals into steps

  6. Actions & Tools

    • APIs, data operations, system effects
    • Tool schemas, safety checks, logging

  7. World Model

    • Explicit state representations and domain knowledge
    • Rules and constraints

  8. Objectives & Constraints

    • Goals, success criteria
    • Alignment, safety policies, guardrails

  9. Orchestration

    • Single or multi-agent composition
    • Routing, specialization, error handling

Understanding each of these components—and how they interact—gives you a concrete framework to:

  • Design new agents more systematically
  • Debug and improve existing ones
  • Evaluate vendor offerings beyond marketing language
  • Reason about safety, reliability, and scalability

Further Reading and Resources

For deeper exploration of AI agents and their architecture:

  • Papers & Concepts

    • “ReAct: Synergizing Reasoning and Acting in Language Models” – Yao et al.
    • “Toolformer: Language Models Can Teach Themselves to Use Tools” – Schick et al.
    • “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models” – Wei et al.
    • “Early Agent Systems: A Survey” – various overviews of classical agent architectures.

  • Frameworks to Explore (Vendor-Neutral View)

    • LangChain and similar libraries for tool-using LLM agents
    • OpenAI / Anthropic / other LLM tool-calling APIs
    • Vector databases (FAISS, Chroma, pgvector, Qdrant) for long-term memory

Studying these with the “anatomy” described above in mind will help you see where each piece fits—and where current systems still fall short of truly general, autonomous intelligence.