AI Agent Architecture

AI agent architecture is the arrangement of components that allows an AI agent to interpret a goal, select actions, execute them and retain the results. A typical LLM agent architecture combines a language model for decisions, a tool layer for actions, memory for state and a control loop that coordinates them under explicit guardrails.

• Model: The reasoning component, usually a large language model, that selects the next action.
• Tools: Functions and APIs that retrieve information or modify external systems.
• Memory: Storage for the current task state and for knowledge that persists between tasks.
• Control loop: Application code, often called the orchestrator, that invokes the model, executes tools and records observations.
• Guardrails: Permission checks, iteration limits and approval rules that constrain the agent's behaviour.

For example, a DevOps agent receives a latency alert, queries metrics and logs, recalls a previous incident and opens a ticket with the probable cause.

Key Components of AI Agent Architecture

• Instructions and goal: A system prompt defines the agent's role, permitted tools and output format, while the task supplies the specific objective.
• Model: The model converts the goal and the accumulated context into a decision, usually a tool call expressed as structured output.
• Tool layer: Each tool has a name, a description and a parameter schema, and tool calling connects the model's request to the implementation.
• Short-term memory: The sequence of actions and observations for the current task, typically stored in the context window.
• Long-term memory: Persistent knowledge, such as previous incidents or runbooks, retrieved when relevant, as covered in memory in AI agents.
• Planning: An optional component that decomposes complex objectives into ordered subtasks, described in planning in AI agents.

How AI Agent Architecture Works

1. Initialisation: The control loop loads the instructions, the tool definitions and any relevant long-term memory.
2. Decision: The model receives the goal and the current context, then returns one tool call or a final answer.
3. Validation: The control loop verifies that the requested tool exists, the arguments are valid and the action is permitted.
4. Execution: The tool layer performs the operation and returns a result or an error message.
5. Observation: The result is appended to short-term memory so the next decision can use it.
6. Termination: The loop stops at a final answer, an iteration limit or an action that requires human approval.

The ReAct agent pattern is the most common implementation of this decision and observation cycle.

Example: An AI Agent Architecture Skeleton in Python

The following program implements the four core components as small classes and prints a trace of one execution.

# AI agent architecture skeleton: model, tools, memory and a control loop.
# Standard library only. Model.decide is a rule-based stand-in for an LLM.
class Tools:
    def __init__(self):
        self.registry = {"get_metrics": self.get_metrics, "read_logs": self.read_logs,
                         "open_ticket": self.open_ticket}

    def get_metrics(self, service):
        return "p95 latency 2400 ms (normal 300 ms)"

    def read_logs(self, service):
        return "WARN db pool exhausted: 20/20 connections in use"

    def open_ticket(self, title, note):
        return f"OPS-2291 created: {title} ({note})"

    def call(self, name, **args):
        return self.registry[name](**args)

class Memory:
    def __init__(self, past_incidents):
        self.short_term = []             # this run's steps
        self.long_term = past_incidents  # lessons from earlier runs

    def add(self, tool, result):
        self.short_term.append((tool, result))

    def recall(self, text):
        return next((fix for key, fix in self.long_term.items() if key in text), "none")

class Model:
    def decide(self, goal, memory):
        # Returns a tool call as structured data, as an LLM would.
        done = [tool for tool, _ in memory.short_term]
        if "get_metrics" not in done:
            return {"tool": "get_metrics", "args": {"service": "orders-api"}}
        if "read_logs" not in done:
            return {"tool": "read_logs", "args": {"service": "orders-api"}}
        if "open_ticket" not in done:
            fix = memory.recall(memory.short_term[-1][1])
            return {"tool": "open_ticket", "args": {"title": "orders-api slow: db pool full",
                                                    "note": f"past fix: {fix}"}}
        return {"tool": "finish", "args": {}}

class Agent:
    def __init__(self, model, tools, memory, max_steps=5):
        self.model, self.tools, self.memory, self.max_steps = model, tools, memory, max_steps

    def run(self, goal):
        print("GOAL", goal)
        for step in range(1, self.max_steps + 1):
            call = self.model.decide(goal, self.memory)
            print(f"[{step}] model  -> {call['tool']} {call['args']}")
            if call["tool"] == "finish":
                return
            result = self.tools.call(call["tool"], **call["args"])
            self.memory.add(call["tool"], result)
            print(f"[{step}] tool   -> {result}")
        print("STOP step limit reached")

memory = Memory({"db pool exhausted": "raise pool size to 40, runbook RB-12"})
Agent(Model(), Tools(), memory).run("Explain the latency alert on orders-api")

GOAL Explain the latency alert on orders-api
[1] model  -> get_metrics {'service': 'orders-api'}
[1] tool   -> p95 latency 2400 ms (normal 300 ms)
[2] model  -> read_logs {'service': 'orders-api'}
[2] tool   -> WARN db pool exhausted: 20/20 connections in use
[3] model  -> open_ticket {'title': 'orders-api slow: db pool full', 'note': 'past fix: raise pool size to 40, runbook RB-12'}
[3] tool   -> OPS-2291 created: orders-api slow: db pool full (past fix: raise pool size to 40, runbook RB-12)
[4] model  -> finish {}

• Separation of responsibilities: The model only proposes tool calls, while the agent control loop in the Agent class executes them and enforces the iteration limit.
• Two memory types: Short-term memory records this execution, and long-term memory contributes a remedy from a previous incident.
• Replaceable model: A real LLM can replace Model.decide without changing the tools, the memory or the control loop.

Applications of AI Agent Architecture

• Coding agents: Repository search, file editing and test execution tools combined with a review step.
• Incident triage: Metrics, logs and ticketing tools combined with a knowledge base of earlier incidents.
• Data quality monitoring: Query tools and validation rules for detecting and reporting anomalies.
• Internal support agents: Documentation retrieval combined with account and permission tools.
• Multi-agent designs: Several agents with separate tools and memory, coordinated as described in multi-agent systems.

Advantages

• Modularity: A modular AI agent design lets each component be tested, replaced or upgraded independently.
• Controllability: Guardrails in the control loop apply regardless of the model's output.
• Observability: Every decision and tool result can be logged for debugging and auditing.
• Reusability: The same loop and memory can serve different agents with different tool sets.

Limitations

• Integration effort: Every tool requires a schema, error handling and access control.
• Context growth: Short-term memory expands with each iteration, which increases cost and can exceed the context window.
• Retrieval quality: Irrelevant long-term memories can mislead the model's decisions.
• Error propagation: An incorrect early observation can influence every subsequent decision.
• Security boundaries: Tool results can contain injected instructions, so permissions must be enforced outside the model, as explained in prompt injection.