Coordinating multiple specialized agents to conduct complex penetration testing engagements through task decomposition, parallel execution, result aggregation, and conflict resolution.
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Coordinating multiple specialized agents to conduct complex penetration testing engagements through task decomposition, parallel execution, result aggregation, and conflict resolution.
payloads.md — Task decomposition templates, agent role definition prompts, coordinator dispatch templates, result aggregation JSON schema, deduplication checklist, conflict resolution decision tree, and coverage verification matrix
test-cases.md — Structured test cases for parallel recon, multi-target aggregation, deduplication conflicts, coverage auditing, and full coordinator-worker engagements
guides/coordinated-pentest-playbook.md — Deep-dive guide on agent role design, task decomposition methodology, communication protocol, result integration, quality gates, and common failure modes
Summary
Multi Agent Collaboration skill domain covering infrastructure operations.
Coordinating multiple specialized agents to conduct complex penetration testing engagements through task decomposition, parallel execution, result aggregation, and conflict resolution. Where a single agent works iteratively, multi-agent collaboration fans out work across specialized instances and fans the results back in — achieving coverage at a speed and depth that no single agent can match.
Critical distinctions from related skills:
Skill
Core Mechanic
Input/Output Relationship
council
Debate — same question, multiple analytical lenses
Same input → multiple perspectives → synthesized judgment
autonomous-loops
Iteration — single agent, repeating patterns
One agent → loop construct → sequential or batched results
multi-agent-collaboration
Decomposition — different tasks to different agents
Multi-agent collaboration is about who does what, not about how one agent thinks or iterates.
Use Cases
Large-Scope Engagements — Enterprise networks with dozens of subnets, hundreds of hosts, and multiple service families (web, database, OT, cloud) exceed single-agent throughput; decompose by attack surface and run in parallel
Multi-Target Assessments — Client owns 5 separate web applications; assign one specialized web tester agent per application, aggregate findings into unified report
Time-Constrained Red Teams — 24-hour window to cover a 50-host network; compress wall-clock time by running recon, scanning, and initial exploitation in parallel across agent instances
Comprehensive Attack Surface Coverage — Simultaneous coverage of network perimeter (network-pentest), web entry points (web-*), credential risks (password-attack), and supply chain exposure (supply-chain-security) without sequential bottlenecks
Verification at Scale — After initial findings from multiple workers, assign a dedicated verification agent to confirm all Critical/High findings independently
Specialized Tool Families — When engagement requires deep expertise in binary analysis AND web testing AND OSINT simultaneously — assign agents by tool specialization rather than forcing one agent to context-switch
Collaboration Models
Model 1: Attack Phase Decomposition
Divide the engagement into phases and assign each phase to a specialized agent. Some phases can run in parallel (recon and initial web discovery); others are sequentially dependent (exploitation requires scan results).
Phase Assignment:
[Recon Agent] ← runs first or in parallel with Web Discovery
[Scan Agent] ← depends on Recon results
[Web Discovery Agent]← can run in parallel with Recon
[Exploit Agent] ← depends on Scan + Web Discovery results
[Post-Exploit Agent] ← depends on successful Exploit results
[Report Agent] ← depends on all prior results
Best for: Single complex target where deep sequential attack chains are needed but individual phases can be parallelized internally.
Trigger conditions: Single target, depth over breadth, phased penetration methodology required.
Model 2: Target Parallelization
Assign each distinct target (host, application, subnet) to an independent agent instance. Each agent runs a complete assessment on its target, then results are aggregated.
One orchestrator agent holds the master scope, decomposes tasks, dispatches to N worker agents, monitors progress, handles failures, and aggregates results. Workers report back in structured format; coordinator maintains the master finding list.
Best for: Complex engagements where task dependencies shift dynamically, worker failures need rerouting, and a single authority must maintain engagement state.
Trigger conditions: High task complexity, dynamic dependencies, need for centralized state management.
Decomposition Principles
When to Use Multi-Agent Collaboration
Apply multi-agent collaboration when two or more of the following are true:
Before assigning tasks to parallel agents, verify independence:
Data dependency check — Does Agent B need Agent A's output to begin? If yes: sequential, not parallel.
Resource conflict check — Do Agents A and B write to the same evidence file or target the same service simultaneously? If yes: coordinate access or serialize.
Scope overlap check — Do both agents touch the same host/port/endpoint? If yes: assign explicit ownership or accept intentional duplication.
State mutation check — Does Agent A's action change target state in ways that affect Agent B's results? If yes: order them or document the dependency.
Tasks that pass all four checks are safe for true parallelization.
Granularity Guidelines
Granularity
Problem
Signal
Too coarse
Agents block each other waiting for shared resources
Agent idle time > 30%
Correct
Agents run independently, minimal coordination overhead
Steady progress, results accumulating
Too fine
Coordination overhead (dispatch, format, aggregate) exceeds task execution time
Tasks complete in <2 minutes
Practical rule: Tasks smaller than 10 minutes of work are typically too fine-grained for separate agents — batch them together or use a loop pattern instead.
Result Aggregation
Deduplication
When multiple agents report findings on the same target:
Normalize titles — Map variant names to canonical finding titles (e.g., "SQL Injection" = "SQLi" = "SQL injection via GET parameter")
Match on target + evidence — Same CVE/technique on same host:port = duplicate candidate
Merge, do not discard — Keep the higher-confidence report as primary; append the secondary as corroboration evidence
Escalate severity — If Agent A rates Medium and Agent B rates High on the same finding, escalate to High and flag for coordinator review
Document provenance — Record which agents reported each finding for audit trail
Conflict Resolution
When agents disagree on vulnerability status (one says vulnerable, one says not):
Gather both evidence sets — Read both agents' command output, screenshots, and methodology notes
Check methodology differences — Different tools, different payloads, different timing? Methodology gap explains most conflicts
Check version/configuration — Was target state consistent between both agents' tests? (Service restart, WAF rule change, etc.)
Retest with third agent — If conflict persists after evidence review, dispatch a dedicated verification agent with explicit instructions to test both approaches
Document uncertainty — If conflict cannot be resolved, report as "Unconfirmed — Requires Manual Verification" at the higher severity, with both evidence sets attached
Escalate to human — Conflicts on Critical findings always escalate to human operator before closing
Coverage Verification
After all agents return results, audit for gaps using the Coverage Matrix (see payloads.md):
Every scope item must map to at least one assigned agent
Every agent result must map back to a scope item
Any scope item with zero findings must be explicitly reviewed — absence of findings is not the same as absence of vulnerability
Cross-tenant memory: Abuse multi-tenant agent runtime to access other tenant's shared state.
Orchestration
ECC Loop Pattern: Batch Processing
Rationale: Multi-agent collaboration is inherently a batch distribution problem — the coordinator fans out tasks to specialized workers, monitors parallel execution, and fans in results for aggregation. The Batch Processing pattern from autonomous-loops provides exactly this dispatch/aggregate structure. The coordinator itself runs a Batch Processing loop; each worker may run any loop pattern appropriate to its assigned task.
Consumes from: council (strategic model selection — which collaboration model fits this engagement), autonomous-loops (each worker agent runs its own appropriate loop pattern), safety-guard (scope enforcement applied independently to every agent)
Cross-Skill Pipeline:
council
→ [strategic decision: which collaboration model to use, scope risk assessment]
↓
multi-agent-collaboration
→ [decompose scope into parallel tasks, assign agents by model]
↓
[Worker A: network-pentest] [Worker B: web-*] [Worker C: osint]
(each runs autonomous-loops pattern internally)
↓ (parallel execution, results returned to coordinator)
multi-agent-collaboration
→ [aggregate findings, run deduplication, resolve conflicts]
↓
verification-loop
→ [independent confirmation of all Critical/High findings]
↓
chronicle + article-writing
→ [consolidated engagement report]
Quality Gate: Before declaring a multi-agent engagement complete, verify all of the following:
