What is an AI Agent: Architecture, Components, and Patterns for Autonomous LLM Systems

TL;DR — What is an AI agent: autonomous LLM systems. Databricks: "Goal-directed software entity that perceives environment, reasons, takes actions. Maintains state, decides which LLMs or tools to invoke, adjusts based on feedback. Four stages: perceive, reason, act, reflect." arXiv 2601.12560: "Agentic AI: autonomous entities that perceive, reason, plan, act. LLMs as cognitive controllers with memory, tool use, feedback. Taxonomy: Perception, Brain, Planning, Action, Tool Use, Collaboration." arXiv 2601.02749: "Iterative perception-reasoning-action loops. ReAct: reason-act-reflect pattern. Three categories: reasoning, action, multi-agent. Long-term planning, contextual memory, tool invocation." arXiv 2503.23037: "Agentic LLMs: reason, act, interact. Agency = identity, control, capability to act on goals. Retrieval enables tool use, reflection improves collaboration." Springer: "Four components: profile, memory, planning, action. Feedback-driven system. CoT and ToT for planning. Single-agent (ReAct, Toolformer) vs multi-agent (MetaGPT, CAMEL)." Learn more with LLM gateway, function calling, tool loop, and multi-agent.

Databricks defines: "An AI agent is a goal-directed software entity that perceives its environment through inputs — text, data streams, API responses, sensor feeds — and takes actions to achieve a defined objective. Unlike a static model that maps inputs to outputs, an AI agent maintains state across interactions, decides which large language models or external tools to invoke, and adjusts its approach based on feedback from previous actions."

arXiv 2601.12560 frames the shift: "Artificial Intelligence is moving from models that only generate text to Agentic AI, where systems behave as autonomous entities that can perceive, reason, plan, and act. Large Language Models (LLMs) are no longer used only as passive knowledge engines but as cognitive controllers that combine memory, tool use, and feedback from their environment to pursue extended goals."

AI Agent Architecture

Agent vs Traditional LLM

Agent Types

Implementation

from dataclasses import dataclass
from typing import Optional
from enum import Enum

class AgentType(Enum):
    REASONING = "reasoning"
    ACTION = "action"
    MULTI_AGENT = "multi_agent"
    HYBRID = "hybrid"

@dataclass
class AIAgentGuide:
    """AI Agent architecture and implementation guide."""

    def get_react_loop(self) -> str:
        """ReAct pattern: reason-act-observe loop."""
        return (
            "import json\n"
            "from openai import OpenAI\n"
            "\n"
            "client = OpenAI()\n"
            "\n"
            "TOOLS = [\n"
            "    {\n"
            "        'type': 'function',\n"
            "        'function': {\n"
            "            'name': 'search',\n"
            "            'description':\n"
            "                'Search the web',\n"
            "            'parameters': {\n"
            "                'type': 'object',\n"
            "                'properties': {\n"
            "                    'query': {\n"
            "                        'type': 'string'\n"
            "                    }\n"
            "                },\n"
            "                'required': ['query']\n"
            "            }\n"
            "        }\n"
            "    },\n"
            "    {\n"
            "        'type': 'function',\n"
            "        'function': {\n"
            "            'name': 'calculate',\n"
            "            'description':\n"
            "                'Do math',\n"
            "            'parameters': {\n"
            "                'type': 'object',\n"
            "                'properties': {\n"
            "                    'expression': {\n"
            "                        'type': 'string'\n"
            "                    }\n"
            "                },\n"
            "                'required': ['expression']\n"
            "            }\n"
            "        }\n"
            "    }\n"
            "]\n"
            "\n"
            "def run_agent(\n"
            "    goal: str,\n"
            "    max_steps: int = 10\n"
            "):\n"
            "    '''ReAct loop: reason,\n"
            "    act, observe, reflect.'''\n"
            "    messages = [\n"
            "        {'role': 'system',\n"
            "         'content': 'You are an'\n"
            "          ' autonomous agent.'\n"
            "          ' Use tools to achieve'\n"
            "          ' the goal. Reason step'\n"
            "          ' by step.'},\n"
            "        {'role': 'user',\n"
            "         'content': goal}\n"
            "    ]\n"
            "    \n"
            "    for step in range(\n"
            "        max_steps):\n"
            "        # 1. Reason + decide\n"
            "        response = client.chat\\\n"
            "            .completions.create(\n"
            "            model='gpt-4o',\n"
            "            messages=messages,\n"
            "            tools=TOOLS)\n"
            "        \n"
            "        msg = response\\\n"
            "            .choices[0].message\n"
            "        messages.append(msg)\n"
            "        \n"
            "        # 2. Check if done\n"
            "        if not msg.tool_calls:\n"
            "            return msg.content\n"
            "        \n"
            "        # 3. Act: execute tools\n"
            "        for call in msg\\\n"
            "            .tool_calls:\n"
            "            result = execute_tool(\n"
            "                call.function.name,\n"
            "                json.loads(\n"
            "                    call.function\n"
            "                    .arguments))\n"
            "            # 4. Observe\n"
            "            messages.append({\n"
            "                'role': 'tool',\n"
            "                'tool_call_id':\n"
            "                    call.id,\n"
            "                'content': str(\n"
            "                    result)\n"
            "            })\n"
            "        # 5. Reflect happens\n"
            "        # in next LLM call\n"
            "    \n"
            "    return 'Max steps reached'"
        )

    def get_components(self) -> dict:
        """Core agent components."""
        return {
            "perception": (
                "Ingest inputs from APIs, "
                "databases, user queries, "
                "data streams. Structured "
                "(SQL), semi-structured "
                "(JSON), unstructured "
                "(documents), streaming "
                "(event queues)."
            ),
            "brain": (
                "LLM cognitive core: "
                "chain-of-thought reasoning, "
                "goal decomposition, tool "
                "selection, action planning. "
                "Determines what to do next "
                "based on context and "
                "prior steps."
            ),
            "planning": (
                "Task decomposition: CoT "
                "(stepwise reasoning), ToT "
                "(tree search). Feedback-free "
                "or feedback-based iteration. "
                "Plans adapt using signals "
                "from environment, human "
                "input, or memory reflections."
            ),
            "action": (
                "Execute plans: API calls, "
                "tool invocation, code "
                "execution, content "
                "generation, delegation to "
                "another agent. Results feed "
                "back into environment."
            ),
            "tool_use": (
                "External tools and APIs, "
                "MCP servers, web search, "
                "database queries, file "
                "operations. Open standards "
                "like MCP replacing fixed "
                "API calls."
            ),
            "memory": (
                "Short-term: current "
                "conversation context. "
                "Long-term: past interactions "
                "and learned patterns. "
                "Working: task state across "
                "steps. Feedback: outcome "
                "learning for future "
                "decisions."
            ),
        }

    def get_agent_taxonomy(self) -> dict:
        """Agent classification."""
        return {
            "reasoning_agents": (
                "Internal cognition: "
                "reflection, goal "
                "decomposition, memory-based "
                "planning. Example: Reflexion "
                "— self-reflection improves "
                "performance over iterations."
            ),
            "action_agents": (
                "Interface with tools, APIs, "
                "or robotic systems. "
                "Examples: Toolformer, ReAct "
                "— interleave reasoning with "
                "tool calls for concrete tasks."
            ),
            "multi_agent": (
                "Coordinate multiple agents "
                "through communication, "
                "negotiation, role "
                "specialization. Examples: "
                "MetaGPT, CAMEL, AgentBoard "
                "— cooperative agents with "
                "distinct roles."
            ),
            "hybrid": (
                "Combines reasoning + action "
                "+ collaboration. Production "
                "agents increasingly integrate "
                "all three capabilities for "
                "complex workflows."
            ),
        }

    def get_challenges(self) -> dict:
        """Open challenges in agentic AI."""
        return {
            "hallucination_in_action": (
                "Agent hallucinates tool "
                "names, parameters, or "
                "results. Mitigation: "
                "validate tool schemas, "
                "verify outputs."
            ),
            "infinite_loops": (
                "Agent stuck in reasoning "
                "or action loop without "
                "progress. Mitigation: "
                "max_steps limit, "
                "progress detection."
            ),
            "prompt_injection": (
                "Malicious input manipulates "
                "agent behavior. Mitigation: "
                "input sanitization, "
                "permission boundaries, "
                "human approval gates."
            ),
            "observability": (
                "Difficult to debug multi-step "
                "agent decisions. Mitigation: "
                "log reasoning traces, "
                "tool calls, and outcomes "
                "at each step."
            ),
            "cost_control": (
                "Multi-step loops consume "
                "many tokens. Mitigation: "
                "budget limits, early "
                "termination, caching."
            ),
        }

    def get_four_stage_loop(self) -> dict:
        """Databricks four-stage loop."""
        return {
            "perceive": (
                "Agent perceives environment, "
                "ingesting inputs from APIs, "
                "databases, user queries, "
                "or real-time data streams."
            ),
            "reason": (
                "Agent reasons over inputs "
                "using LLM or planning module "
                "to determine best next action."
            ),
            "act": (
                "Agent acts by calling a tool, "
                "writing to a system, "
                "generating content, or "
                "delegating to another agent."
            ),
            "reflect": (
                "Agent reflects on outcome, "
                "updating understanding of "
                "task state and feeding "
                "learning into next "
                "perception cycle."
            ),
            "loop": (
                "This loop runs until goal "
                "is reached or human operator "
                "takes control."
            ),
        }

AI Agent Checklist

• [ ] AI agent: goal-directed software entity that perceives, reasons, and acts autonomously
• [ ] Unlike static LLM: maintains state, decides which tools to invoke, adjusts based on feedback
• [ ] Agentic AI: autonomous entities that perceive, reason, plan, and act with memory and tool use
• [ ] LLMs as cognitive controllers: combine memory, tool use, feedback to pursue extended goals
• [ ] Agentic LLMs: (1) reason, (2) act, (3) interact — agency = identity, control, capability to act
• [ ] Six core components: Perception, Brain (LLM), Planning, Action, Tool Use, Memory
• [ ] Perception: ingest inputs from APIs, databases, user queries, data streams
• [ ] Perception: structured (SQL), semi-structured (JSON), unstructured (documents), streaming (events)
• [ ] Brain/LLM: cognitive core — chain-of-thought reasoning, goal decomposition, tool selection
• [ ] Planning: task decomposition via CoT (stepwise) and ToT (tree search)
• [ ] Planning: feedback-free (prompting) or feedback-based (environment signals, human input, reflections)
• [ ] Action: execute via API calls, tool invocation, code execution, content generation, delegation
• [ ] Tool Use: external tools, APIs, MCP servers, web search, database queries, file operations
• [ ] Memory: short-term (current context), long-term (past interactions), working (task state), feedback (learning)
• [ ] Four-stage loop (Databricks): perceive → reason → act → reflect → repeat until goal reached
• [ ] ReAct pattern: reason-act-observe-reflect loop, interleaving reasoning traces with tool calls
• [ ] ReAct: (1) Reason about current state, (2) Act by calling tool, (3) Observe result, (4) Reflect, (5) Repeat
• [ ] ReAct and Toolformer: transform static LLM into adaptive, interactive agent
• [ ] Reflexion: self-reflection improves performance over iterations
• [ ] Agent vs LLM: multi-step loop vs single response, stateful vs stateless, tools vs text, feedback vs none
• [ ] Agent vs LLM: autonomous and goal-directed vs passive, planning vs none, auditable vs black box
• [ ] Three agent types: reasoning (internal cognition), action (tool interface), multi-agent (coordination)
• [ ] Hybrid agents: combine reasoning + action + collaboration for complex workflows
• [ ] Single-agent systems: Reflexion, Toolformer, ReAct — decision loops with planning, memory, tool use
• [ ] Multi-agent systems: MetaGPT, CAMEL, AgentBoard — specialized agents with distinct roles
• [ ] Multi-agent: structured communication, reflective reasoning, explicit role assignments
• [ ] Open challenges: hallucination in action, infinite loops, prompt injection, observability, cost control
• [ ] Mitigations: validate tool schemas, max_steps limit, input sanitization, log reasoning traces, budget limits
• [ ] MCP (Model Context Protocol): open standard replacing fixed API calls for tool use
• [ ] Native Computer Use: agents interacting with operating systems directly
• [ ] Environments: digital operating systems, embodied robotics, specialized domains
• [ ] Feedback-driven system: memory shapes planning, actions modify memory, update operational profile
• [ ] Retrieval enables tool use, reflection improves multi-agent collaboration, reasoning benefits all
• [ ] Agentic architectures make intermediate decisions observable and auditable
• [ ] Long-term planning, contextual memory, tool invocation = collaborative problem solvers
• [ ] Read LLM gateway guide for infrastructure
• [ ] Read function calling for tool use
• [ ] Read tool loop for ReAct implementation
• [ ] Read multi-agent system for coordination
• [ ] Test: agent completes multi-step task using tools
• [ ] Test: agent stops at max_steps without infinite loop
• [ ] Test: memory persists across interaction steps
• [ ] Test: reflection improves outcome on retry
• [ ] Test: reasoning traces are logged and auditable
• [ ] Document agent type, tools, memory strategy, max_steps, and fallback behavior

FAQ

What is an AI agent?

An AI agent is a goal-directed software entity that perceives its environment, reasons about goals, and takes actions to achieve them. Databricks: "An AI agent is a goal-directed software entity that perceives its environment through inputs — text, data streams, API responses, sensor feeds — and takes actions to achieve a defined objective. Unlike a static model that maps inputs to outputs, an AI agent maintains state across interactions, decides which LLMs or external tools to invoke, and adjusts its approach based on feedback from previous actions." arXiv 2601.12560: "Artificial Intelligence is moving from models that only generate text to Agentic AI, where systems behave as autonomous entities that can perceive, reason, plan, and act. LLMs are no longer used only as passive knowledge engines but as cognitive controllers that combine memory, tool use, and feedback from their environment to pursue extended goals." arXiv 2503.23037: "Agentic LLMs are LLMs that (1) reason, (2) act, and (3) interact. Agency is about identity and control, and the capability to act on one goals. Agents are endowed with decision-making capabilities, they communicate, sense changes in the environment, and act upon those changes."

What are the core components of an AI agent architecture?

AI agents have six core components: perception, brain (LLM), planning, action, tool use, and memory. arXiv 2601.12560: "Unified taxonomy: Perception, Brain, Planning, Action, Tool Use, and Collaboration. The move from linear reasoning procedures to native inference time reasoning models, and from fixed API calls to open standards like MCP and Native Computer Use." Springer: "Four core components: profile definition, memory, planning, and action execution — together form a feedback-driven system where memory shapes planning, actions modify memory, and update the agent operational profile. Planning modules decompose complex tasks using CoT and Tree-of-Thought." Databricks: "Four stages: perceive (ingest inputs), reason (LLM decides next action), act (call tools, write to systems), reflect (update understanding). Perception layer: structured, semi-structured, unstructured, streaming sources. Reasoning layer: LLMs anchor this layer, paired with specialized planners." Components: (1) Perception: ingest inputs from APIs, databases, streams. (2) Brain/LLM: cognitive core for reasoning and planning. (3) Planning: decompose tasks via CoT, ToT. (4) Action: execute via API calls, tool invocation, code execution. (5) Tool Use: call external tools and APIs. (6) Memory: maintain state across interactions.

What is the ReAct pattern for AI agents?

ReAct interleaves reasoning traces with tool calls in a reason-act-reflect loop. arXiv 2601.02749: "ReAct and Toolformer implement this loop by explicitly interleaving reasoning traces with tool calls. This transforms a static LLM into an adaptive, interactive agent. A recurrent reason-act-reflect pattern: LLM Brain (Reasoning and Planning) determines what to do next. Action: execution through API calls, tool invocation, code execution. Actions feed results back into the environment, completing the feedback loop." Springer: "Single-agent systems such as Reflexion, Toolformer, and ReAct showed how models can operate in decision loops that involve planning, memory, and tool use. Feedback-based iteration allows agents to dynamically adapt plans using signals from the environment, human input, or memory reflections." ReAct loop: (1) Reason: LLM generates thought about current state. (2) Act: LLM selects and calls a tool. (3) Observe: tool returns result. (4) Reflect: LLM updates understanding. (5) Repeat until goal reached.

How is an AI agent different from a traditional LLM?

Traditional LLMs generate one-shot text responses. AI agents operate through iterative perception-reasoning-action loops with memory, tool use, and feedback. arXiv 2601.02749: "Unlike traditional LLMs which generate one-shot text responses, agentic AI operates through iterative perception-reasoning-action loops. Agents decompose complex tasks, interact with external environments, and refine actions based on feedback. Capabilities like long-term planning, contextual memory, and tool invocation enable agents to function as collaborative problem solvers rather than passive text generators. By decomposing tasks into explicit reasoning, action, and reflection steps, agentic architectures make intermediate decisions more observable and auditable." Databricks: "Where conventional AI tools wait for a prompt and return a single response, agentic systems operate as persistent actors: they perceive context, reason over objectives, call external tools, and refine their behavior based on outcomes. The defining characteristic is autonomous decision making: the system determines how to reach a goal without requiring constant human oversight at each intermediate step." Key differences: (1) LLM: single request → single response. Agent: multi-step loop until goal reached. (2) LLM: no state. Agent: maintains memory across interactions. (3) LLM: text only. Agent: calls tools, APIs, code. (4) LLM: no feedback. Agent: adapts based on outcomes. (5) LLM: passive. Agent: autonomous and goal-directed.

What are the types of AI agents?

AI agents are classified into three functional categories: reasoning, action, and multi-agent. arXiv 2601.02749: "Three functional categories: (1) Reasoning agents which perform internal cognition such as reflection, goal decomposition, and memory-based planning. (2) Action agents that interface with tools, APIs, or robotic systems to perform concrete tasks. (3) Multi-agent or interactive systems which coordinate multiple agents through communication, negotiation, or role specialization. Hybrid systems increasingly combine these capabilities." arXiv 2503.23037: "Three categories: reasoning (reflection, retrieval, decision making), action (action models, robots, tools — agents as useful assistants), interaction (multi-agent systems for collaborative task solving and simulating emergent social behavior). Works mutually benefit: retrieval enables tool use, reflection improves multi-agent collaboration, reasoning benefits all categories." Springer: "Single-agent systems (Reflexion, Toolformer, ReAct) operate in decision loops. Multi-agent systems (MetaGPT, CAMEL, AgentBoard) — multiple LLMs interact as specialized agents with distinct roles, collaborating to solve complex tasks through structured communication, reflective reasoning, and explicit role assignments." Types: (1) Reasoning agent: internal cognition, reflection, planning. (2) Action agent: tool use, API calls, code execution. (3) Multi-agent: coordinated agents with specialized roles. (4) Hybrid: combines reasoning + action + collaboration.

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