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We have reached a point in AI development where "Model Monotheism"—the belief that one model can rule them all—is dead.

In 2026, the engineering reality is heterogeneous. We have Claude Code for deep architectural reasoning, GPT-5.2 for nuanced planning, and Gemini 3 Pro for massive context absorption.

The challenge is no longer about prompting a single model; it's about Orchestration. How do we build a "Super Agent" (or Meta-Agent) that utilizes these three distinct brains as a cohesive unit?

The Cast: Defining Roles

To build an effective team, we must first understand the unique "cognitive profile" of our workers.

1. The Architect: Claude Code

Strength: Extended thinking, recursive debugging, and maintaining complex mental models of codebases.
Role: Writing implementation code, refactoring, and solving "impossible" bugs.
Trigger: When the task involves write_code, debug, or architecture_design.

2. The Strategist: GPT-5.2

Strength: Superior instruction following, user intent analysis, and high-level planning. It has the highest "EQ" for understanding vague requirements.
Role: The Project Manager. It breaks down the user's prompt into a DAG (Directed Acyclic Graph) of tasks.
Trigger: Initial request handling, plan refinement, and final synthesis.

3. The Scholar: Gemini 3 Pro

Strength: Infinite context window (now standard at 10M+) and multimodal speed.
Role: The Researcher and QA. It reads the entire documentation library, analyzes logs, and performs "needle in a haystack" retrieval.
Trigger: read_docs, analyze_logs, context_retrieval.

The Architecture: The "Council of Experts" Pattern

We don't want a linear chain. We want a Hub-and-Spoke architecture with a shared state.

Core Components

The Router (The Brain): Usually a lightweight instance of GPT-5.2 or a fine-tuned classifier. It decides who gets the ticket.
The Shared State (The Whiteboard): A Redis or Vector Database instance that holds the "World View"—current file contents, plan status, and memory.
The Workers: The API wrappers for our three giants.

The Workflow

graph TD
    User["User Request"] --> Router["Router (GPT-5.2)"]
    Router -->|"Planning"| PM["GPT-5.2 Agent"]
    Router -->|"Context Needed"| Res["Gemini 3 Agent"]
    Router -->|"Coding Needed"| Dev["Claude Code Agent"]
    
    PM -->|"Update Plan"| State[("Shared State")]
    Res -->|"Inject Context"| State
    Dev -->|"Commit Code"| State
    
    State -->|"Feedback Loop"| Router

Implementation: The Routing Logic

Here is a simplified Python pseudo-code for the Orchestrator loop:

class SuperAgent:
    def __init__(self):
        self.state = SharedMemory()
        self.router = RouterModel(model="gpt-5.2-turbo")

    async def run(self, user_goal):
        self.state.update(goal=user_goal)
        
        while not self.state.is_complete():
            # 1. Observe current state
            context = self.state.get_snapshot()
            
            # 2. Decide next step and worker
            action = await self.router.decide(context)
            
            # 3. Dispatch to specialist
            if action.type == "RESEARCH":
                result = await GeminiAgent.execute(action.payload)
            elif action.type == "CODE":
                result = await ClaudeCodeAgent.execute(action.payload)
            elif action.type == "PLAN":
                result = await GPTAgent.execute(action.payload)
                
            # 4. Update state
            self.state.apply(result)
            
        return self.state.final_output()

The Secret Sauce: Consensus & Handoffs

The hardest part isn't calling the APIs; it's the Handoff.

The "Context Baton"

When Gemini finishes reading 50 PDF files, it shouldn't pass 10M tokens to Claude (that's expensive and slow). Instead, it must generate a Compressed Insight—a high-density summary or a specific code snippet—to pass to the Architect.

Voting Mechanism

For critical decisions (e.g., "Delete Production DB"), we implement a Voting Protocol.

GPT-5.2 proposes a plan.
Claude Code reviews it for technical feasibility.
Gemini checks it against historical incidents.
Action is taken only if 3/3 agree.

Conclusion

The future of AI engineering isn't about waiting for a model that has 200 IQ in every domain. It's about building the Management Layer that coordinates specialized savants.

By treating Claude, GPT, and Gemini as functional components in a larger system, we move from "Chatbot" to "Digital Workforce."

The AI Triumvirate: Building a Super-Agent Orchestrator