The 15-Agent Architecture: How Proxie Ships Faster Than Consulting Firms

McKinsey assigns 1 analyst to your project. We deploy 15 AI agents. This isn't a gimmick — it's an architecture decision that changes the economics of consulting.

The Problem with Sequential Work

Traditional consulting follows a waterfall: research → analysis → synthesis → recommendations → deliverable. Each step waits for the previous one. A 6-month engagement isn't 6 months of work — it's 2 months of work stretched across 6 months of dependencies and scheduling conflicts.

The dirty secret of consulting: junior analysts do 70% of the work. They research, they analyze, they build decks. Partners show up for the kickoff and the final presentation. You're paying partner rates for analyst labor.

Parallel Execution: Why 15 Agents Beat 1 Smart Person

Our swarm doesn't work sequentially. When we start a competitive analysis, here's what happens simultaneously:

• ▸3 research agents pull market data, competitor info, and technical specifications — at the same time
• ▸As data arrives, 3 analysis agents start processing — they don't wait for all research to finish
• ▸Build agents begin structuring the deliverable framework while analysis is running
• ▸QA agents validate facts as they're produced, not after everything is done

The orchestrator manages dependencies: analysis can't synthesize data that hasn't been researched yet. But it can start on the data that's already in. This is pipelining — the same concept that makes modern CPUs fast.

Meet the Swarm

Research Agents (3)

• ▸Market Research Agent: Pulls industry reports, market sizing, trend data
• ▸Competitor Intelligence Agent: Analyzes competitor products, pricing, positioning
• ▸Technical Research Agent: Evaluates tech stacks, architectures, patent filings

Analysis Agents (3)

• ▸Data Synthesis Agent: Cross-references findings, identifies patterns
• ▸Gap Analysis Agent: Maps whitespace and missed opportunities
• ▸Opportunity Mapping Agent: Scores and prioritizes opportunities by impact

Build Agents (4)

• ▸Content Agent: Drafts reports, narratives, executive summaries
• ▸Code Agent: Builds prototypes, scripts, automation tools
• ▸Design Agent: Creates visual frameworks, diagrams, data visualizations
• ▸Documentation Agent: Compiles technical specs, API docs, process flows

QA Agents (3)

• ▸Fact-Check Agent: Validates every claim against source data
• ▸Security Scanner: Runs proxie.in code security analysis on any generated code
• ▸Quality Review Agent: Checks brand voice, logical consistency, completeness

Orchestrator + Human Review (2)

• ▸Orchestrator Agent: Manages task distribution, dependency resolution, pipeline scheduling
• ▸Human Review Layer: Final approval on all deliverables — no AI slop ships without human eyes

The Orchestrator: How It Works

class SwarmOrchestrator:
    def execute(self, project_brief: str):
        # Decompose project into parallel work streams
        tasks = self.planner.decompose(project_brief)
        dependency_graph = self.planner.build_dag(tasks)

        # Execute tasks respecting dependencies
        for batch in dependency_graph.topological_batches():
            # All tasks in a batch run in parallel
            results = parallel_execute([
                agent.run(task)
                for task, agent in self.assign_agents(batch)
            ])

            # QA runs on each result immediately
            validated = [self.qa_pipeline(r) for r in results]

            # Failed QA? Re-route to different agent
            for r in validated:
                if not r.passed:
                    self.retry_with_escalation(r)

        # Human review gate
        return self.human_review.submit(validated)

Timeline Comparison

Real Example: Competitive Analysis in 48 Hours

A fintech startup needed a competitive landscape analysis covering 40+ competitors across 3 markets. Traditional approach: 4-6 weeks with a 2-person analyst team. Our swarm: 48 hours.

3 research agents pulled data on all 40 competitors simultaneously. Analysis agents identified 12 whitespace opportunities the client hadn't considered. The deliverable included a 50-page report, competitive matrix, and strategic recommendations — all fact-checked and human-reviewed.

The Human Layer

Here's what we don't automate: strategic judgment. The swarm produces the raw materials — research, analysis, first drafts. Humans make the calls: which opportunities to prioritize, how to frame recommendations for the client's board, what nuances the data doesn't capture.

AI + humans > AI alone. Every time. The swarm makes humans faster, not obsolete.