Orchestrating Multi Agent AI Swarms

For any non-trivial coding task, start with anti-drift swarm config rather than ad-hoc agent spawning:

JavaScript
swarm_init({
  topology: "hierarchical",   // single coordinator validates all output
  maxAgents: 8,                // smaller team = less drift surface
  strategy: "specialized"      // clear, non-overlapping roles
})

// Then route the task to a role-appropriate agent set, e.g. bug fix:
spawn_agents(["coordinator", "researcher", "coder", "tester"])

Before reaching for a full swarm, check if the task is simple enough to skip the LLM entirely (WASM transform) or cheap enough for a small model (Haiku). Only escalate to multi-agent swarms + large models for genuinely complex, multi-step work.

Progress:

• Step 1: Classify task complexity (simple / medium / complex)
• Step 2: Pick the cheapest capable handler
• Step 3: Choose topology + consensus algorithm if multi-agent
• Step 4: Define agent roles with non-overlapping responsibilities
• Step 5: Wire up shared memory namespace + checkpoints
• Step 6: Execute with verification gates between steps
• Step 7: Capture successful patterns back into memory for reuse

Step 1 — Classify complexity:

Never spawn a swarm for something a direct model call or static transform can handle — this is the single biggest cost/latency lever.

Step 2 — Route to cheapest capable handler. Escalate only on failure or detected ambiguity, don't default to the biggest model.

Step 3 — Choose topology for complex tasks:

Choose consensus algorithm by fault-tolerance need:

• Majority — low-stakes decisions, fast
• Weighted (coordinator gets ~3x vote) — default for most dev tasks
• Byzantine fault-tolerant (f < n/3) — security audits, irreversible actions, untrusted agent outputs

Step 4 — Define agent roles. Map task type to a minimal, non-overlapping agent set (don't over-provision):

Step 5 — Shared memory + checkpoints. All agents in a swarm read/write to one shared memory namespace. Insert checkpoint/verification gates after each agent's output, before the next agent consumes it — this is what actually prevents drift, not just the topology choice.

Step 6 — Execute with short cycles. Keep each agent's task cycle short (single clear deliverable) and verify against the original goal before continuing. The coordinator rejects and re-routes outputs that drift.

Step 7 — Capture learning. Store successful task→agent→outcome patterns (vector memory / pattern store) so future similar tasks route directly to what worked, skipping re-exploration.

Example 1: Input: "Convert this file's var declarations to const/let across 40 lines." Output: Classified as Simple → route to WASM/AST transform directly (no agent, no LLM call). Output applied in <1ms, $0 cost. No swarm needed.

Example 2: Input: "Add a new OAuth login flow, including backend route, frontend form, and tests." Output: Classified as Complex → hierarchical topology, maxAgents=5, agent set = [coordinator, architect, coder, tester, reviewer], weighted consensus, shared memory namespace oauth-login-feature, checkpoint after architect's design before coder starts, checkpoint after coder before tester starts.

Example 3: Input: "Audit the payment processing module for security vulnerabilities." Output: Classified as Complex + security-sensitive → hierarchical topology, agent set = [coordinator, security-architect, auditor], Byzantine consensus (f < n/3) required since findings must be fault-tolerant and trustworthy before acting on them.