msd-core/docs/explanation/multi-agent-orchestration.md

Multi-agent orchestration in GSD Core

Explanation — This document describes why GSD Core is designed around multi-agent orchestration and how the pieces fit together. It is not a step-by-step guide. For configuration, see Configure model profiles and the Configuration reference. For the full agent roster, see Inventory.

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The problem this design solves

AI coding agents degrade. Not because the model gets worse, but because the context window fills up. As a conversation grows, earlier decisions and code get pushed out or diluted by the noise of intermediate steps. By the time an agent writes the fifth file in a complex task, it may have already forgotten the constraint stated in the first message. This is sometimes called context rot.

GSD Core's multi-agent design is a direct response to that problem. Instead of one long-running agent carrying the whole session, a thin orchestrator spawns short-lived specialised agents, each with a fresh 200 K-token context window and only the artifacts it needs to do its specific job. The orchestrator never does heavy lifting itself; it loads context, spawns the right agent, collects the result, and updates shared state in .planning/.

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The orchestrator → agent pattern

Every workflow in gsd-core/workflows/ follows the same shape:

Orchestrator (workflow .md file)
    │
    ├── Load context
    │   gsd-tools.cjs init <workflow> <phase>
    │   → JSON: project info, config, state, phase details
    │
    ├── Resolve model
    │   gsd-tools.cjs resolve-model <agent-name>
    │   → opus | sonnet | haiku | inherit
    │
    ├── Spawn specialised agent (Task/SubAgent call)
    │   ├── Agent definition (agents/*.md)
    │   ├── Context payload (init JSON)
    │   ├── Model assignment
    │   └── Tool permissions
    │
    ├── Collect result
    │
    └── Update state
        gsd-tools.cjs state update / state patch / state advance-plan

The orchestrator is deliberately thin. It does not reason about the domain, does not write code, and does not interpret results beyond routing them to the next step. That boundary keeps each layer's responsibility clear and prevents the orchestrator's context from accumulating domain noise.

The agent roster

GSD Core's agents fall into functional categories that map onto the research → plan → execute → verify pipeline:

Category	Agents	Typical parallelism
Researchers	gsd-project-researcher, gsd-phase-researcher, gsd-ui-researcher, gsd-advisor-researcher	4 parallel (stack, features, architecture, pitfalls)
Synthesisers	gsd-research-synthesizer	Sequential, after researchers complete
Planners	gsd-planner, gsd-roadmapper	Sequential
Checkers	gsd-plan-checker, gsd-integration-checker, gsd-ui-checker, gsd-nyquist-auditor	Sequential, up to 3 revision iterations
Executors	gsd-executor	Parallel within a wave, sequential across waves
Verifiers	gsd-verifier	Sequential, after all executors complete
Mappers	gsd-codebase-mapper	4 parallel sub-probes
Auditors	gsd-ui-auditor, gsd-security-auditor	Sequential

Each agent definition (in agents/*.md) declares its allowed tool access, purpose, and colour for terminal output. An agent that only needs to read files and write a single output document gets exactly those permissions — no Bash execution, no access to broader state. That constraint is intentional: it keeps the blast radius small if an agent behaves unexpectedly.

For the complete 31-agent roster, see Inventory.

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Wave-based parallel execution

The most visible expression of multi-agent design is how /gsd-execute-phase handles a set of plans that may depend on one another.

Before spawning any executor, the orchestrator performs a wave analysis: it reads the dependency declarations in each PLAN.md file and groups plans into waves. Plans with no declared dependencies form Wave 1 and run in parallel. Plans that depend on Wave 1 form Wave 2, and so on.

Plan 01 (no deps)        ─┐
Plan 02 (no deps)        ─┤─── Wave 1  (parallel)
Plan 03 (depends: 01)    ─┤─── Wave 2  (waits for Wave 1)
Plan 04 (depends: 02)    ─┘
Plan 05 (depends: 03, 04) ─── Wave 3  (waits for Wave 2)

Each executor within a wave:

• receives a fresh context window (200 K tokens, or up to 1 M on capable models)
• receives the specific PLAN.md it is responsible for
• receives project context (PROJECT.md, STATE.md)
• receives phase context (CONTEXT.md, RESEARCH.md if available)
• produces atomic git commits on completion
• writes a SUMMARY.md describing what was built

After all executors in a wave finish, the orchestrator runs the pre-commit hook once for the wave as a whole. Executors commit with --no-verify to prevent build-lock contention (for example, Cargo lock fights in Rust projects) when multiple agents commit in parallel. The hook therefore runs once per wave rather than once per commit.

Parallel commit safety

Two mechanisms prevent write conflicts when multiple executors run simultaneously:

1. Atomic lock on STATE.md — Every write to STATE.md uses a lockfile (STATE.md.lock) with O_EXCL atomic creation. This prevents the read-modify-write race where two agents each read the file, modify different fields, and the later writer overwrites the earlier one's changes. Stale locks (older than 10 seconds) are automatically cleared.

2. Per-wave hook run — Rather than each executor running pre-commit hooks independently (which can cause file-level contention on shared build artefacts), the orchestrator runs git hook run pre-commit once after every wave completes.

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Adaptive context enrichment for large-window models

Standard 200 K context windows are enough for an executor to implement a single focused plan. When the configured context_window is 500 K tokens or larger (for example, when using Opus 4.6 or Sonnet 4.6 in 1 M-class mode), the orchestrator automatically enriches subagent prompts with additional context that would not fit in a standard window:

• Executor agents receive prior-wave SUMMARY.md files and the phase CONTEXT.md/RESEARCH.md, giving them cross-plan awareness within the phase
• Verifier agents receive all PLAN.md, SUMMARY.md, and CONTEXT.md files plus REQUIREMENTS.md, enabling history-aware verification

This enrichment is conditional on the context_window value in config.json. On standard-window configurations, prompts use truncated versions with cache-friendly ordering to maximise token efficiency.

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Why this design — the connection to context engineering

The orchestrator → agent pattern only makes sense as part of a broader approach to context engineering: the idea that what an AI agent gets in its context window matters as much as the model tier or prompt quality. See Context engineering for the full treatment.

Multi-agent orchestration operationalises context engineering in two ways:

Context isolation. Each agent receives only what it needs. A researcher gets the project description and domain questions; it does not get the full planning history. A verifier gets every plan and summary; it does not get the raw research. Isolation keeps each agent's context dense with signal rather than diluted by noise from other pipeline stages.

Context hygiene across sessions. Because all state lives in .planning/ as human-readable Markdown and JSON (not in any agent's context window), GSD workflows survive context resets (/clear), tab switches, and multi-day breaks. The next agent always starts from persisted, verified artifacts rather than from a reconstructed memory of a long conversation.

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Trade-offs

Multi-agent orchestration is not free.

Coordination overhead. Each agent spawn is a round-trip: the orchestrator must format a prompt, hand off context, wait for the subagent to complete (typically 1–5 minutes), and then parse the result. A single capable agent working in one context would finish faster for simple tasks. GSD mitigates this by making parallelism the default wherever dependencies permit — the four researchers in a plan-phase run simultaneously, not sequentially.

Opacity during execution. While a subagent is running, its work is invisible to the parent session. There is no live progress stream. This is a deliberate consequence of the fresh-context design: the subagent is operating in its own context window. The orchestrator shows a liveness note on the spawn line ("runs in a subagent — no output until it returns") to set expectations.

Context stitching cost. Packaging the right artifacts for each agent requires the orchestrator to spend tokens assembling and transmitting context payloads. This is the cost of isolation. The gsd-tools.cjs init handler produces a JSON payload that balances completeness with token budget, applying cache-friendly ordering so that the stable parts of the payload (project definition, config) hit the cache on repeat invocations.

Model cost amplification. Running five agents in parallel at Opus tier costs more than running one. The model profile system (model_profiles.md, resolved per agent by model-profiles.cjs) lets you assign cheaper tiers to less critical agents. The dynamic_routing feature further reduces cost by starting every agent on a cheaper tier and escalating only on a soft failure. See Configuration for the full options.

In return for these costs, the design buys consistent quality across large phases. An executor writing the tenth file in a 400-line plan does not degrade because its context is fresh. A verifier checking twenty requirements does not forget the first ten because it received all of them as structured input rather than conversation history.

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Related

• Context engineering — the upstream principle that motivates this design; see also Lifecycle hooks and context headroom for how per-turn headroom tracking and forked-context skills extend the same principle at runtime
• Configure model profiles — how to assign model tiers per agent
• Configuration reference — full config.json schema including models, model_overrides, dynamic_routing, and context_window
• Inventory — authoritative agent roster and workflow list
• Architecture — implementation-level detail on the orchestrator → agent pattern and wave execution model
• Docs index