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AI Engineering · Version 1.1.0 · Reviewed 2026-08-02

Multi-Agent Execution Orchestrator

Make AI behavior measurable and safer for agent task graph planning and parallel execution control with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Plans dependency-aware multi-agent work with bounded parallelism, ownership isolation, checkpoints, and deterministic integration. It grounds the decision in the task dependency graph, file ownership, resource limits, and required completion evidence and explicitly prevents parallel agents editing the same surface or reporting success without satisfying a shared gate.

₹199 one-time

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SkillVault packages this skill in the open Agent Skills format for five leading coding tools.

What this skill helps you do

  • Agent task graph planning
  • Parallel execution control
  • Sub-agent result integration

How Multi-Agent Execution Orchestrator works

You provide

The objective, task graph, agent capabilities, ownership boundaries, budgets, and completion evidence

It inspects

Ready dependencies, write-set overlap, and retry safety for agent task graph planning

It decides

A parallel execution control plan with leases, checkpoints, bounded parallelism, and an integration gate

You verify

Agent loss and duplicate delivery are recoverable, and the integrated artifact passes the shared acceptance contract

What it checks first

Multi-Agent Execution Orchestrator plans dependency-aware multi-agent work with bounded parallelism, ownership isolation, checkpoints, and deterministic integration. It grounds the decision in the task dependency graph, file ownership, resource limits, and required completion evidence and explicitly prevents parallel agents editing the same surface or reporting success without satisfying a shared gate. Use it when the work involves Agent task graph planning, Parallel execution control, Sub-agent result integration.

  1. The task dependency graph and which tasks are actually ready, rather than the number of agents available.
  2. Write-set and resource ownership overlap, since independent prompts can still race on shared files or state.
  3. Capability and result contracts that distinguish completed, failed, blocked, cancelled, and partially useful work.
  4. Checkpoint and lease behavior when an agent, process, or conversation disappears mid-task.
  5. The integration gate that validates combined behavior instead of trusting individually successful summaries.

Failure modes it recognizes

  • Two agents claim the same task or edit the same surface, producing a last-writer-wins result with hidden lost work.
  • A downstream agent starts from a predecessor summary before the predecessor artifact passes its completion gate.
  • An agent reports success after a tool failure because the orchestration contract accepts prose instead of evidence.
  • Recursive delegation expands without a depth, time, cost, or total-agent budget.
  • A resumed run repeats a non-idempotent action because checkpoints record intent but not the committed side effect.
  • All lanes pass independently while their combined changes violate a shared interface or acceptance criterion.

Answers it will reject

  • Maximizing parallelism before mapping dependencies and overlapping write sets.
  • Using one shared mutable worktree or state document as the communication bus for every agent.
  • Treating a sub-agent response as completion without validating the declared artifact and evidence contract.
  • Launching more agents to resolve ambiguity that requires one accountable product or architecture decision.

Decision rules it applies

  • Parallelize only ready tasks with isolated ownership; keep pattern-defining and shared-contract changes serial.
  • Require idempotent task identifiers and durable checkpoints before supporting retry or resume.
  • Integrate through artifacts and objective gates, not through confidence language in agent summaries.
  • Stop delegation when the next uncertainty is a human-owned decision, unavailable evidence, or exhausted budget.

Evidence it asks for

  • Record task state transitions, owner, dependency version, artifact digest, tool outcomes, and completion evidence.
  • Visualize the dependency graph and compute ready, blocked, leased, failed, and orphaned tasks per checkpoint.
  • Inject agent loss, duplicate delivery, stale context, and integration conflicts into orchestration tests.
  • Track total agent time, tool calls, retries, context cost, and accepted output per completed task.

The method inside

  1. Establish what is actually true about agent task graph planning from the supplied evidence, and mark what is missing.
  2. Identify the mechanism behind parallel execution control rather than restating the symptom.
  3. Choose the smallest defensible change for sub-agent result integration, weighing impact, confidence, effort, and reversibility.
  4. Measure quality, safety, latency, and cost separately

Deliverables

  • Agent task graph planning assessment
  • Parallel execution control decision and action plan
  • Sub-agent result integration verification checklist

Evidence requirements

  • Prompts, model/version, tools, retrieval path, and examples
  • Evaluation dataset and failure cases
  • Latency, cost, privacy, and policy constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Apply the multi-agent execution orchestrator to our current agent task graph planning work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with the task dependency graph, file ownership, resource limits, and required completion evidence. The highest-risk failure is parallel agents editing the same surface or reporting success without satisfying a shared gate. Dispatch only ready tasks whose write sets do not overlap, and integrate results against one authoritative acceptance contract. Verify the result by reconstructing the run from checkpoints and proving each merged result came from a completed dependency path.

Boundaries and compatibility

Ideal for

  • Agent task graph planning: produce a decision or artifact grounded in supplied evidence.
  • Parallel execution control: produce a decision or artifact grounded in supplied evidence.
  • Sub-agent result integration: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating prompt text as a security boundary
  • Claiming model quality from a handful of demos

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.