coordinate-swarm（坐标群）

Coordinate Swarm

Establish coordination across distributed agents using stigmergy (indirect communication through environment modification), local interaction rules, and quorum sensing — enabling coherent collective behavior without a central controller.

When to Use

• Designing distributed systems where no single node should be a coordination bottleneck
• Organizing teams or workflows that must self-coordinate without constant management oversight
• Building event-driven architectures where components communicate through shared state rather than direct messaging
• Scaling a process that works well with 3 agents but breaks down at 30
• Bootstrapping coordination patterns for a new swarm-style domain (see forage-resources, build-consensus)
• Replacing fragile centralized orchestration with resilient emergent coordination

Inputs

• Required: Description of the agents (workers, services, team members) that need coordination
• Required: The collective goal or desired emergent behavior
• Optional: Current coordination mechanism and its failure modes
• Optional: Number of agents (affects pattern selection — small swarms vs. large colonies)
• Optional: Latency tolerance (real-time vs. eventual coordination)
• Optional: Environmental constraints (shared state availability, communication bandwidth)

Procedure

Step 1: Identify the Coordination Problem Class

Classify the coordination challenge to select appropriate patterns.

1. Map the current state: who are the agents, what do they do individually, where does coordination break down?
2. Classify the problem:

- Foraging — agents search for and exploit distributed resources (see forage-resources) - Consensus — agents must agree on a collective decision (see build-consensus) - Construction — agents build or maintain a shared structure incrementally - Defense — agents detect and respond to threats collectively (see defend-colony) - Division of labor — agents must self-organize into specialized roles

1. Identify the failure mode of current coordination:

- Single point of failure (centralized controller) - Communication bottleneck (too many direct messages) - Coherence loss (agents drift apart without feedback) - Rigidity (cannot adapt to changing conditions)

Expected: A clear classification of the coordination problem type and the specific failure mode to address. This determines which swarm patterns to apply.

On failure: If the problem doesn't fit a single class, it may be a composite. Decompose into sub-problems and address each with the appropriate pattern. If agents are too heterogeneous for a single coordination model, consider layered coordination — homogeneous clusters coordinated via inter-cluster stigmergy.

Step 2: Design Stigmergic Signals

Create the indirect communication channels through which agents influence each other's behavior.

1. Define the shared environment (database, message queue, file system, physical space, shared board)
2. Design signals that agents deposit into the environment:

- Trail signals: markers that accumulate along successful paths (like ant pheromones) - Threshold signals: counters that trigger behavior changes when they cross thresholds - Inhibition signals: markers that repel agents from exhausted areas

1. Define signal properties:

- Decay rate: how quickly signals fade (prevents stale state from dominating) - Reinforcement: how successful outcomes strengthen signals - Visibility radius: how far a signal propagates

1. Map signals to agent behaviors:

- When an agent detects signal X above threshold T, it performs action A - When an agent completes action A successfully, it deposits signal Y - When no signal is detected, the agent follows its default exploration behavior

Signal Design Template:
┌──────────────┬───────────────────┬──────────────┬────────────────────┐
│ Signal Name  │ Deposited When    │ Decay Rate   │ Agent Response     │
├──────────────┼───────────────────┼──────────────┼────────────────────┤
│ success-trail│ Task completed OK │ 50% per hour │ Follow toward      │
│ busy-marker  │ Agent starts task │ On completion│ Avoid / pick other │
│ help-signal  │ Agent stuck >5min │ 25% per hour │ Assist if nearby   │
│ danger-flag  │ Error detected    │ 10% per hour │ Retreat & report   │
└──────────────┴───────────────────┴──────────────┴────────────────────┘

Expected: A signal table mapping environmental markers to agent deposit conditions, decay rates, and response behaviors. Signals should be simple, composable, and independently meaningful.

On failure: If signal design feels overly complex, reduce to two signals: one positive (success trail) and one negative (danger flag). Most coordination problems can be bootstrapped with attract/repel dynamics. Add nuance only after the basic system is functioning.

Step 3: Define Local Interaction Rules

Specify the simple rules each agent follows, using only local information (their own state + nearby signals).

1. Define the agent's perception radius (what can it sense?)
2. Write 3-7 local rules in priority order:

- Rule 1 (safety): If danger-flag detected, move away - Rule 2 (response): If help-signal detected and idle, move toward - Rule 3 (exploitation): If success-trail detected, follow toward strongest signal - Rule 4 (exploration): If no signals detected, move randomly with bias toward unexplored areas - Rule 5 (deposit): After completing task, deposit success-trail at location

1. Each rule must be:

- Local: depends only on what the individual agent can perceive - Simple: expressible in one if-then statement - Stateless (preferred): does not require the agent to remember past states

1. Test rules mentally: if every agent follows these rules, does the desired collective behavior emerge?

Expected: A prioritized rule set that each agent executes independently. When applied across the swarm, these local rules produce the target collective behavior (foraging, construction, defense, etc.).

On failure: If mental simulation doesn't produce the desired emergent behavior, the rules likely need a feedback loop — agents must be able to observe the consequences of their collective actions. Add a signal that represents the collective state (e.g., "task completion rate") and a rule that adjusts behavior based on it.

Step 4: Calibrate Quorum Sensing

Set thresholds that trigger collective state changes when enough agents agree.

1. Identify decisions that require collective agreement (not just individual response):

- Switching from exploration to exploitation mode - Committing to a new work site or abandoning an old one - Escalating from normal to emergency response

1. For each collective decision, define:

- Quorum threshold: number or percentage of agents that must signal agreement - Sensing window: time period over which signals are counted - Hysteresis: different thresholds for activation vs. deactivation (prevents oscillation)

1. Implement quorum as signal accumulation:

- Each agent that favors the decision deposits a vote-signal - When accumulated votes exceed the quorum threshold within the sensing window, the decision activates - When votes drop below the deactivation threshold, the decision reverses

Expected: Quorum thresholds that allow the swarm to make collective decisions without a leader. The hysteresis gap prevents rapid oscillation between states.

On failure: If the swarm oscillates between states, widen the hysteresis gap (e.g., activate at 70%, deactivate at 30%). If the swarm never reaches quorum, lower the threshold or increase the sensing window. If decisions are too slow, reduce the sensing window — but beware of premature consensus.

Step 5: Test and Tune Emergent Behavior

Validate that local rules produce the desired collective behavior, then tune parameters.

1. Run a simulation or pilot with a small number of agents (5-10)
2. Observe:

- Does the swarm converge on the intended behavior? - How long does convergence take? - What happens when conditions change mid-task? - What happens when agents fail or are added?

1. Tune parameters:

- Signal decay rate: too fast → no coordination memory; too slow → stale signals dominate - Quorum threshold: too low → premature collective decisions; too high → paralysis - Exploration-exploitation balance: too much exploration → inefficient; too much exploitation → local optima

1. Stress test:

- Remove 30% of agents suddenly — does the swarm recover? - Double the agent count — does the swarm still coordinate? - Introduce conflicting signals — does the swarm resolve or deadlock?

Expected: A tuned parameter set where the swarm self-organizes toward the target behavior, recovers from perturbations, and scales gracefully.

On failure: If the swarm fails stress tests, the signal design is likely too tightly coupled. Simplify: reduce to fewer signals, increase decay rates (fresher information), and ensure agents have a robust default behavior when no signals are present. A swarm that does something reasonable with zero signals is more resilient than one that depends on signal availability.

Validation

• Coordination problem is classified into a recognized pattern (foraging, consensus, construction, defense, division of labor)
• Stigmergic signal table is defined with deposit conditions, decay rates, and agent responses
• Local interaction rules are simple, local, and prioritized (3-7 rules)
• Quorum thresholds are set with hysteresis to prevent oscillation
• Small-scale test shows emergent behavior matching the collective goal
• Stress test (agent removal, addition, signal disruption) shows graceful degradation

Common Pitfalls

• Over-engineering signals: Starting with too many signal types creates confusion. Begin with 2 signals (attract/repel) and add only when proven necessary
• Centralized thinking in disguise: If your "local rule" requires an agent to know the global state, it's not local. Refactor until each rule depends only on what the agent can directly perceive
• Ignoring decay: Signals that never decay create fossilized coordination state. Every signal needs a half-life appropriate to the task's time scale
• Zero hysteresis: Quorum thresholds without a gap between activation and deactivation cause rapid state oscillation. Always set deactivation lower than activation
• Assuming homogeneity: If agents have different capabilities, a single rule set may not work. Consider role-differentiated rules (see scale-colony)

Related Skills

• forage-resources — applies swarm coordination specifically to resource search and explore-exploit tradeoffs
• build-consensus — deep dive into distributed agreement mechanisms, extending the quorum sensing from this skill
• defend-colony — collective defense patterns that build on the signal and rule framework here
• scale-colony — scaling strategies for when the swarm outgrows its initial coordination design
• adapt-architecture — morphic skill for transforming system architecture, complementary when swarm coordination triggers structural change
• deploy-to-kubernetes — practical distributed system deployment where swarm coordination patterns apply
• plan-capacity — capacity planning informed by swarm scaling dynamics
• coordinate-reasoning — AI self-application variant; maps stigmergic signals to context management with information decay rates and local protocols