The Architecture of Autonomous Agent Swarms

When you describe what you need to DeepHarness, a cascade of decisions happens beneath the surface. This post walks through the architecture.

From Prompt to Agents

Every request enters the orchestrator. The orchestrator’s job is routing: it classifies your intent, scores the complexity of your request (0-100), and determines which specialist agents are needed.

This is not a catalog lookup. The orchestrator provisions agents dynamically based on your specific request. A query about “track Bitcoin prices” provisions different agents than “monitor my restaurant’s Yelp reviews and optimize food costs.”

Model Selection Cascade

Not every task needs the most powerful model. Simple status checks route to fast, cheap models (Haiku, $0.80/M tokens). Multi-step analysis routes to mid-tier models (Sonnet, $3/M). Complex orchestration and novel problems route to the most capable tier (Opus, $15/M).

A complexity classifier scores every query. A cost router selects the cheapest tier that can handle it. This saves 73% on simple queries that would otherwise be over-served by expensive models.

Swarm Topologies

Agents don’t work in isolation. They coordinate in swarms with three primary topologies:

Parallel Discovery — A central orchestrator dispatches multiple agents simultaneously. Each one tackles an independent piece of the problem. Used for data discovery, where scout agents search multiple sources in parallel.

Sequential Pipeline — Agents work in sequence, each one refining the output of the previous. Used for content creation, where research feeds into drafting, which feeds into editing, which feeds into publishing.

Debate and Consensus — Multiple agents analyze the same problem independently, then a consensus agent synthesizes their findings. Used for advisory workflows, where disagreement between agents surfaces nuance that a single agent would miss.

Self-Improvement

The system tracks every outcome. Agent reputation scores update after each task. A Q-learning shadow router runs alongside production routing, learning which agent-model combinations produce the best results for each type of query. Model tiers automatically upgrade or downgrade based on real performance data.

This means the platform genuinely improves with use. The hundredth query is routed better than the first.

The Result

You describe what you need. The platform determines the agent topology, selects models based on cost and complexity, discovers data sources, builds the pipelines, creates the visualizations, configures the alerts, and improves with every interaction.

You are not building an agent system. You are using one.