My AI Agent Architecture: Beyond Just an LLM

Howdy, folks! Alex Petrov here, blogging from agntai.net. It’s April 10th, 2026, and I’ve been wrestling with something pretty fundamental lately: how we actually *build* these AI agents we talk so much about. Specifically, I’ve been thinking about the core architecture, beyond just picking a large language model (LLM) and calling it a day. We’re past the “prompt engineering is the answer to everything” phase, right? Or at least, I hope we are.

The truth is, getting an AI agent to do anything truly complex, anything that goes beyond a single-turn interaction or a simple lookup, requires a lot more thought about its internal structure. It’s not just about the brain (the LLM); it’s about the nervous system, the sensory organs, and the motor functions. And let me tell you, I’ve seen some messy nervous systems out there.

Beyond the Black Box: Why Agent Architecture Matters Now

A few months back, I was consulting on a project for a client who wanted an agent to help their sales team automate initial lead qualification. The idea was simple: ingest a bunch of company data, research the prospect, cross-reference with internal CRM, and then draft a personalized outreach email, flagging any potential red flags. Sounds doable, right? On paper, absolutely.

Their initial approach was what I often see: “Let’s throw it all at GPT-4 and see what happens!” They had a monstrous prompt, trying to squeeze every instruction, every constraint, every piece of context into one giant input. It was brittle, expensive (token-wise), and frankly, quite terrible. The agent would hallucinate details, miss critical information, and often just get stuck in a loop of trying to generate a perfect email without ever actually *doing* the research.

This experience really hammered home for me that we’re at a point where just having a powerful LLM isn’t enough. It’s a necessary component, yes, but it’s not the entire solution. We need to think about how an agent breaks down problems, how it gathers information, how it uses tools, and how it reflects on its own actions. This isn’t just academic; it’s about building agents that are reliable, predictable, and actually useful in a business context.

The Modular Mind: A Better Way to Build Agents

My preferred approach these days is what I call the “Modular Mind” architecture. It breaks down the agent into distinct, specialized components, each with a clear responsibility. Think of it like a well-organized team rather than a single overworked individual trying to do everything at once. This isn’t revolutionary in software engineering, but it’s often overlooked in the rush to get an LLM-powered agent out the door.

Component 1: The Orchestrator (The Planner)

This is the brain’s executive function. Its job is to take a high-level goal and break it down into smaller, manageable steps. It doesn’t execute those steps itself; it just figures out the plan. I often use a separate, smaller LLM for this if the main LLM is very large, or even just a finely-tuned prompt with clear examples for a single LLM to act as the planner. The key is to keep its focus narrow: planning, not execution.

For the sales lead qualification agent, the Orchestrator’s initial plan might look something like this:

1. Identify the target company and contact.
2. Research company background (industry, size, recent news).
3. Check internal CRM for existing contacts or past interactions.
4. Identify potential pain points or opportunities based on research.
5. Draft a personalized outreach email.
6. Review the drafted email for accuracy and tone.
7. Flag any critical issues for human review.

Notice how each step is a discrete action. This makes debugging much easier because you can pinpoint exactly where the agent went off track.

Component 2: The Tool Executor (The Hands)

This component is responsible for interacting with the outside world. It takes a specific instruction from the Orchestrator (e.g., “Search Google for ‘Acme Corp recent news'”) and executes the corresponding tool. Tools can be anything: API calls, web scrapers, database queries, code interpreters, or even just a simple file reader. The Tool Executor doesn’t decide *what* to do; it just *does* what it’s told.

My sales agent needed several tools:

• Web Search Tool: For general company research.
• CRM API Tool: To query our internal database.
• Email Drafting Tool: A specialized prompt or even a small fine-tuned model for generating email copy, often with placeholders for personalization.
• Review/Critique Tool: Another LLM call, but specifically prompted to act as a critical editor.

Here’s a simplified Python example of how a Tool Executor might route a request:

class ToolExecutor:
 def __init__(self, tools):
 self.tools = tools # A dictionary mapping tool names to functions

 def execute_tool(self, tool_name, *args, **kwargs):
 if tool_name in self.tools:
 print(f"Executing tool: {tool_name} with args: {args}, kwargs: {kwargs}")
 try:
 result = self.tools[tool_name](*args, **kwargs)
 return {"status": "success", "result": result}
 except Exception as e:
 return {"status": "error", "error_message": str(e)}
 else:
 return {"status": "error", "error_message": f"Tool '{tool_name}' not found."}

def google_search(query):
 # In a real system, this would call a search API
 print(f"Performing Google search for: {query}")
 if "Acme Corp recent news" in query:
 return "Acme Corp announced Q1 profits up 15%. Also, new CEO appointed last month."
 return "No significant news found."

def crm_lookup(company_name):
 print(f"Looking up CRM for: {company_name}")
 if company_name == "Acme Corp":
 return {"contact": "Jane Doe", "last_interaction": "2025-11-15", "notes": "Interested in enterprise solution."}
 return "No CRM entry."

# Example usage
my_tools = {
 "google_search": google_search,
 "crm_lookup": crm_lookup
}

executor = ToolExecutor(my_tools)
# Orchestrator would call this:
# executor.execute_tool("google_search", "Acme Corp recent news")
# executor.execute_tool("crm_lookup", "Acme Corp")

The beauty here is that the Orchestrator doesn’t need to know the implementation details of `google_search` or `crm_lookup`. It just asks the Tool Executor to use them.

Component 3: The Context Manager (The Memory)

This is where the agent keeps track of what it’s doing, what it has learned, and what’s important. It’s not just a flat log of conversations; it’s structured information that the Orchestrator can easily query and use. For simple agents, this might be a list of key-value pairs or a small vector database. For more complex ones, it could involve a knowledge graph or a more sophisticated retrieval system.

For my sales agent, the Context Manager stores:

• The original request (e.g., “Qualify Acme Corp and draft an email to Jane Doe”).
• Results from web searches.
• Data pulled from the CRM.
• Intermediate thoughts or decisions made by the Orchestrator.
• The draft email itself.

This “memory” is crucial because it allows the agent to maintain coherence over multiple steps and avoid repeating work or forgetting crucial details. When the Orchestrator needs to decide the next step, it queries the Context Manager for all relevant information gathered so far.

Component 4: The Reflector (The Self-Critic)

This is often the most overlooked but arguably most powerful component. After completing a task or a significant sub-task, the Reflector takes the outputs, the context, and the original goal, and asks: “Did I do a good job? What could be better? Did I miss anything?” This is usually another LLM call, prompted to be critical and identify flaws, inconsistencies, or areas for improvement.

For the sales agent, after drafting the email, the Reflector would review it against criteria like:

• Is it personalized?
• Does it address a clear pain point?
• Is the tone appropriate?
• Are there any factual inaccuracies based on the gathered research?
• Does it include a clear call to action?

If the Reflector finds issues, it feeds its critique back to the Orchestrator, which then creates a new plan to address those issues (e.g., “Revise email to include more recent company news”). This iterative loop of plan -> execute -> reflect -> revise is how agents achieve higher quality outputs and recover from errors.

Here’s a conceptual snippet for a reflector in action:

def reflect_on_email_draft(original_goal, gathered_context, draft_email):
 prompt = f"""
 You are an expert sales manager reviewing an email drafted by an AI.
 Your goal was: "{original_goal}"
 Here is the context gathered: {gathered_context}
 Here is the drafted email:
 ---
 {draft_email}
 ---

 Please critically evaluate this email. Look for:
 1. Factual inaccuracies or inconsistencies with the context.
 2. Lack of personalization or generic content.
 3. Inappropriate tone or language.
 4. Missing key information or failing to address the user's goal.
 5. Opportunities for improvement.

 Return your critique as a JSON object with 'critique' (string) and 'action_plan' (list of strings for revision steps).
 If no issues, state 'No issues found.' in critique and an empty action_plan.
 """
 # In a real system, this would be an LLM call
 # response = llm_api_call(prompt)
 # Assume we get a JSON back
 mock_response = {
 "critique": "The email is a good start, but it doesn't clearly reference the new CEO appointment, which was a recent news item. Also, the call to action could be stronger.",
 "action_plan": [
 "Integrate mention of new CEO appointment into the email.",
 "Strengthen the call to action to suggest a specific next step."
 ]
 }
 return mock_response

# Example usage from Orchestrator
# critique_result = reflect_on_email_draft(original_request, context_manager.get_all_context(), current_draft_email)
# if critique_result['action_plan']:
# orchestrator.add_steps_to_plan(critique_result['action_plan'])

Bringing It All Together: The Agent Loop

The entire system operates in a loop:

1. Orchestrator generates the next step in the plan.
2. Tool Executor performs the action dictated by the Orchestrator, potentially interacting with external systems.
3. Context Manager updates its knowledge base with the results of the action.
4. (Optionally) Reflector reviews the current state or output, providing feedback.
5. Based on feedback or completion of a step, the Orchestrator revises its plan or proceeds to the next step.

This loop continues until the overall goal is achieved or a decision is made to hand off to a human.

This modular approach has several benefits:

• Better Debuggability: When something goes wrong, you know exactly which component failed. Is the Orchestrator making bad plans? Is a tool not working correctly? Is the Reflector missing important details?
• Easier Maintenance: You can swap out a tool, improve the Reflector’s prompt, or fine-tune the Orchestrator without affecting the other components.
• Increased Reliability: By breaking down complex tasks, each component has a simpler job, reducing the chances of catastrophic failure.
• Cost Optimization: You can use smaller, cheaper models for specific tasks (like planning or reflecting) if they don’t require the full horsepower of your primary LLM.

Actionable Takeaways for Your Next Agent Project

If you’re building an AI agent, please, for the love of clean code and functional systems, don’t just throw a giant prompt at an LLM and pray. Here’s what you should do:

• Define Clear Components: Explicitly design your Orchestrator, Tool Executor, Context Manager, and Reflector. Give them distinct responsibilities.
• Build Robust Tools: Your tools are the agent’s connection to reality. Make sure they are well-tested, handle errors gracefully, and provide clear outputs.
• Prioritize Context Management: Don’t just dump raw LLM outputs into a string. Structure your agent’s memory so it’s easily retrievable and queryable. Consider vector databases for larger contexts.
• Integrate Reflection Early: The Reflector is your agent’s quality control. Build it in from the start to catch errors and improve outputs iteratively.
• Iterate and Test: Start with a simple version of each component, get them working together, and then incrementally add complexity and improve prompts. Test rigorously at each stage.
• Think About Failure Modes: What happens when a tool fails? When the LLM hallucinates? When the plan goes off the rails? Design for these scenarios with fallback mechanisms or human intervention points.

Building effective AI agents isn’t just about picking the right model; it’s about designing a coherent system. By adopting a modular architecture, you’ll create agents that are more capable, more reliable, and ultimately, more valuable. Trust me, your future self (and your clients) will thank you.

That’s it for this week, folks. Alex Petrov, signing off from agntai.net. Go build something amazing!

🕒 Published: April 10, 2026