Commanding AI Swarms

Given a task requiring multiple agents (e.g., "coordinate 5 research agents to produce a verified report"), immediately produce the five deliverables below rather than discussing theory first:

1. Swarm Structure — topology + roles
2. Coordination Flow — message/task lifecycle
3. Consensus Logic — how agents agree
4. Trust Propagation — how confidence/reputation spreads
5. Recovery Strategy — what happens when agents fail or disagree

Progress:

• Step 1: Clarify the swarm's objective, constraints (latency, cost, agent count), and failure tolerance
• Step 2: Define Swarm Structure — topology and agent roles
• Step 3: Define Coordination Flow — task dispatch, message passing, synchronization points
• Step 4: Define Consensus Logic — how conflicting outputs are resolved into one decision
• Step 5: Define Trust Propagation — how agent reliability is scored and weighted over time
• Step 6: Define Recovery Strategy — detection and handling of stalled/faulty/malicious agents
• Step 7: Present all five as a compact, labeled output block

Step 2 details: Swarm Structure

Choose a topology and justify it:

• Star (Commander-led) — single orchestrator dispatches to workers; best for simple tasks, low latency.
• Mesh (Peer-to-peer) — agents communicate directly; best for high redundancy, no single point of failure.
• Hierarchical (Tiered) — sub-commanders manage clusters of workers; best for large swarms (20+ agents).
• Blackboard — agents read/write shared state asynchronously; best for exploratory/creative tasks.

Assign explicit roles: Commander, Specialist(s), Verifier, Aggregator, Watchdog.

Step 3 details: Coordination Flow

Describe as a numbered lifecycle: task decomposition → dispatch → parallel execution → intermediate reporting → aggregation → final synthesis. Note synchronization barriers (e.g., "all specialists report before Verifier runs").

Step 4 details: Consensus Logic

Pick a mechanism matched to task type:

• Majority vote — discrete classification tasks
• Weighted vote by trust score — heterogeneous agent reliability
• Debate/critique rounds — open-ended reasoning tasks
• Commander override — time-critical decisions with tie-break authority

State the quorum threshold (e.g., "requires 3/5 agreement or Commander tiebreak").

Step 5 details: Trust Propagation

Define how a trust/reputation score is initialized, updated (based on accuracy, consistency, peer validation), decayed over time, and used to weight future votes or task assignments.

Step 6 details: Recovery Strategy

Cover: timeout detection, heartbeat/watchdog checks, task reassignment, quarantine of misbehaving agents, graceful degradation (swarm continues with reduced capacity), and rollback of unverified outputs.

Example 1: Input: "Coordinate a swarm of 4 agents to fact-check a news article."

Output:

Swarm Structure:
- Topology: Star (Commander-led)
- Roles: Commander (dispatch+synthesis), 2x Fact-Checker specialists, 1 Verifier, 1 Watchdog

Coordination Flow:
1. Commander splits article into claims
2. Claims dispatched to both Fact-Checkers in parallel
3. Each returns verdict + source citations within timeout
4. Verifier cross-checks citations for validity
5. Commander aggregates into final report

Consensus Logic:
- Weighted vote by trust score between the two Fact-Checkers
- If disagreement >30%, trigger a debate round (each rebuts the other once)
- Commander breaks remaining ties

Trust Propagation:
- Each agent starts at trust=0.7
- +0.05 per verified-correct claim, -0.1 per unverifiable/false claim
- Trust decays 2%/week if agent inactive
- Votes weighted by current trust score

Recovery Strategy:
- 30s timeout per specialist; on timeout, reassign claim to Watchdog fallback
- Verifier flags citation failures → claim marked "unverified," excluded from final report
- If Commander itself fails, highest-trust specialist auto-promotes temporarily

Example 2: Input: "Design a swarm for continuous 24/7 monitoring of server logs with 50 agents."

Output:

Swarm Structure:
- Topology: Hierarchical (5 sub-commanders, each managing 10 log-watcher agents)

Coordination Flow:
1. Log streams sharded across 5 clusters
2. Watchers emit anomaly events to their sub-commander
3. Sub-commanders deduplicate/aggregate hourly
4. Top-level Commander receives cluster summaries, correlates cross-cluster patterns

Consensus Logic:
- Majority vote within cluster for anomaly severity classification
- Cross-cluster correlation requires 2+ sub-commanders reporting related signal

Trust Propagation:
- Watchers scored on false-positive rate; low-trust watchers' alerts require secondary confirmation

Recovery Strategy:
- Heartbeat every 10s; missed 3x → agent marked dead, sub-commander redistributes its shard
- Sub-commander failure triggers automatic re-election among its 10 watchers