Building a Strategic Intelligence Swarm: When AI Agents Own the Boardroom

Moving beyond simple tool-calling to autonomous, stateful multi-agent systems for market intelligence.

TL;DR

In this article, I share my experience building a "Strategic Intelligence Swarm"—a multi-agent system powered by LangGraph that autonomously monitors market trends, analyzes competitive landscapes, and synthesizes executive-level strategic recommendations. I'll walk you through the design decisions, the LangGraph implementation, and the personal insights I gained from moving beyond simple linear agent workflows.

Introduction

From my experience in the AI space, we’ve spent a lot of time talking about "agents" that can call functions. But in my opinion, the real value—and the real challenge—lies in getting agents to work together in complex, non-linear swarms. I've often seen projects struggle because they try to force an LLM to do everything in a single prompt or a simple chain.

I decided to run a PoC to see if I could build something more sophisticated: a swarm that doesn't just "execute" tasks but "coordinates" a strategic mission. I call it the Strategic Intelligence Swarm.

What's This Article About?

This article is a deep dive into my personal experiments with LangGraph to solve a practical business problem: Autonomous Strategic Market Intelligence.

I observed that many enterprise leaders are overwhelmed by the sheer volume of market data. I thought, "What if I could build a swarm of agents where each has a specific role—monitoring, analysis, strategy—and they pass state between each other to build a comprehensive report?"

Throughout this write-up, I'll explain my design choices, share the code I wrote for this PoC, and show you how to structure a multi-agent system that feels less like a script and more like a team... except it's all me, and all AI.

Why Read It?

If you're like me and you've hit the limits of basic LangChain chains or simple OpenAI function calling, this is for you. In my view, the "Swarm" pattern is the next evolution of AI implementation. I wrote this because I want to share the practical hurdles I faced—like state management and loop control—and how I overcame them.

Tech Stack

For this experiment, I chose a stack that provides both flexibility and robustness:

• LangGraph: In my opinion, the best framework for building stateful, multi-agent systems. It allowed me to treat the workflow as a graph.
• Python: My go-to language for anything AI/ML.
• Tavily (or Mock Search): For gathering real-time market data.
• GitHub: For versioning the PoC.

Let's Design

Before I wrote a single line of code, I spent a lot of time thinking about the architecture. I didn't want a linear pipeline. I wanted a loop where agents could refine their findings.

System Architecture Overview

From my perspective, the key to a successful swarm is the Monitor-Analyst-Strategist triage.

1. The Monitor is the eyes; it searches for trends.
2. The Analyst is the brain; it looks at what the competitors are doing.
3. The Strategist is the voice; it synthesizes everything into a recommendation.

I designed it this way because I’ve found that decoupling "data gathering" from "strategic synthesis" leads to much higher-quality results.

Agent Communication Flow

Let’s Get Cooking

Now, let's dive into the code. I put this together as a focused experiment, and I’m quite proud of how clean the state management turned out.

Step 1: Defining the Swarm State

In my experience, the most critical part of LangGraph is the AgentState. This is the single source of truth that every agent reads from and writes to.

from typing import Annotated, List, TypedDict, Union
from typing_extensions import TypedDict

class AgentState(TypedDict):
    # The record of communication
    messages: List[str]
    # The current focus of the mission
    focus: str
    # Research findings gathered along the way
    findings: List[str]
    # Strategic insights produced by the analyst
    insights: List[str]
    # The final strategic report
    report: str
    # The next destination in the graph
    next_step: str

What This Does:
This TypedDict defines the "shared memory" of our swarm. It tracks everything from raw findings to the final report.

Why I Structured It This Way:
I chose to separate findings and insights. From what I’ve seen, if you mix raw data with processed analysis, the LLM starts to get "foggy." By having dedicated keys, I ensure that the Strategist sees the processed logic, while the Analyst sees the raw data.

Step 2: The Agents and Their Logic

I implemented the agents as methods within a class. I find this pattern much easier to manage as the swarm grows.

class StrategicAgents:
    def monitor_agent(self, state: AgentState) -> AgentState:
        """Agent that monitors market trends."""
        print("--- MONITOR AGENT ---")
        query = state.get("focus", "Generative AI")
        # In a real world, this would be a Tavily search
        finding = f"Found rising interest in {query}..."
        state["findings"].append(finding)
        state["next_step"] = "analyst"
        return state

    def analyst_agent(self, state: AgentState) -> AgentState:
        """Agent that analyzes findings."""
        print("--- ANALYST AGENT ---")
        findings = state["findings"]
        analysis = f"Analysis: {findings} suggest a competitive shift."
        state["insights"].append(analysis)
        state["next_step"] = "strategist"
        return state

What I Learned:
One thing I discovered during this PoC is that the next_step key is a lifesaver. Instead of trying to put complex routing logic in the graph itself, I let the agent decide where it thinks the conversation should go next.

Step 3: Orchestrating with LangGraph

This is where the magic happens. I used the StateGraph to wire everything together.

from langgraph.graph import StateGraph, END

def create_swarm() -> StateGraph:
    agents = StrategicAgents()
    workflow = StateGraph(AgentState)

    workflow.add_node("monitor", agents.monitor_agent)
    workflow.add_node("analyst", agents.analyst_agent)
    workflow.add_node("strategist", agents.strategist_agent)

    workflow.set_entry_point("monitor")

    workflow.add_conditional_edges(
        "monitor",
        lambda x: x["next_step"],
        {"analyst": "analyst", "end": END}
    )
    # ... more edges
    return workflow.compile()

Design Decisions I Made:
I decided to use conditional_edges for everything. In my opinion, this makes the swarm truly "autonomous." If the Monitor agent decides it has enough data, it could theoretically skip to the Strategist. This flexibility is what makes LangGraph so powerful compared to linear chains.

Let's Setup

If you want to try this experimental PoC yourself, follow these steps. I've kept it as simple as possible.

Step-by-step details can be found at: https://github.com/aniket-work/strategic-intelligence-swarm

1. Clone the repo.
2. Install the requirements (pip install -r requirements.txt).
3. Set your API keys in a .env file (if you want to use real LLM calls).

Let's Run

I put together a simple main.py to trigger the swarm. When I ran this, I felt a genuine sense of accomplishment. Seeing the agents hand off data—"Monitor" finding trends, "Analyst" parsing them, and "Strategist" writing the report—was a "Eureka" moment for me.

python main.py

Closing Thoughts

Building this Strategic Intelligence Swarm taught me that the future of AI isn't just "smarter" models, but smarter coordination. From my experience, we are moving away from the "Chatbot" era and into the "Agent Swarm" era.

I think the biggest takeaway for me was the importance of state. If you control the state, you control the machine. I hope this PoC inspires you to look at LangGraph not just as a library, but as a blueprint for autonomous strategic thinking.

The full code is available on my GitHub: https://github.com/aniket-work/strategic-intelligence-swarm

Disclaimer

The views and opinions expressed here are solely my own and do not represent the views, positions, or opinions of my employer or any organization I am affiliated with. The content is based on my personal experience and experimentation and may be incomplete or incorrect. Any errors or misinterpretations are unintentional, and I apologize in advance if any statements are misunderstood or misrepresented.

Tags: ai, langgraph, python, multiagent