Multi‑Agent Orchestration: Patterns That Actually Work

Multi‑agent orchestration looks powerful in slides: a planner drafts a plan, executors build, reviewers verify, and the system ships. In practice, the difference between a demo and a durable operation is everything around the agents—ownership, handoffs, SLAs, and approvals.

This post outlines patterns we’ve seen work in production. The goal is not to worship a framework, but to give leaders a practical checklist to reduce variance and increase throughput.

Why multi‑agent vs single‑agent

Single‑agent approaches are simple to start but hit scaling limits: context windows explode, prompts become Frankenstein’s monster, and errors are hard to localize. Multi‑agent patterns decompose work into roles with bounded responsibility and clearer failure modes.

When roles are explicit, you can apply different policies, tools, and models per step, and measure reliability with meaningful metrics instead of one giant success/fail number.

Core orchestration patterns (planner/executor/reviewer)

The most durable pattern is a three‑role loop:

1. Planner creates a plan and acceptance criteria.
2. Executor implements and proposes a diff.
3. Reviewer verifies against criteria and policy, then approves or requests changes.

Enhancements that increase reliability:

• Tool specialists: route certain steps to agents with deep tool expertise (e.g., SQL migrations, CI pipelines). /features
• Parallelism with limits: run independent sub‑tasks concurrently but cap concurrency per repo to avoid stampedes.
• Checkpoints: persist artifacts (plan, test results) as typed events in the timeline.

Coordination models that scale

Different missions call for different coordination models:

• Central planner, local executors: a single planner creates a global plan; executors work per service or component. Useful for cross‑repo refactors.
• Hierarchical planners: a top‑level planner delegates to sub‑planners for complex domains (e.g., data, frontend, infra) to keep prompts short and context focused.
• Review council: multiple specialized reviewers (security, performance) provide targeted feedback before a final approval gate.

The right model keeps context bounded and prevents any single agent from becoming a bottleneck.

Handoffs and ownership

Handoffs are where clarity dies or thrives. A good handoff includes the artifact, its status, and the next decision.

Checklist:

• Artifact contract: what’s passed (plan, diff, report), where it lives, and how to reference it.
• Owner of the next action: which role/agent is responsible, and under what policy.
• Timeout and retry policy: when to escalate, when to backoff, and where to notify.

Clear ownership reduces loops. It also enables partial failure handling: if the reviewer stalls, the mission pauses with a visible reason instead of silently spinning.

Tooling and context boundaries

Agents should have scoped access to tools and files. Keep the executor’s write permissions limited to a branch and a directory subset when possible. Expose read‑only access to production data paths by default, escalating only with an explicit approval. These boundaries make incidents smaller and reviews faster.

SLAs/SLOs and retries for reliability

Agents are software; treat them like services. Define SLAs and SLOs per step and monitor them.

Practical measures:

• Max attempts and backoff per step; switch to a fallback model if quality dips.
• Budgets for tokens, time, and file churn to prevent runaway behavior.
• Health probes for dependencies (package registries, CI) to avoid wasted retries.

Instrument these controls in observability so you can see failure distributions, MTTR, and regressions over time. See /features and /features

Error taxonomies that help

Classify errors in ways that drive action: transient (retry), policy (needs approval), and semantic (needs plan change). This keeps retries from masking policy or design issues.

Anti‑patterns to avoid

• Giant prompts with full repo dumps that vary on every run—leads to non‑determinism and wasted tokens.
• Unbounded parallelism where 20 agents open 20 PRs that step on each other.
• Hidden side effects (e.g., scripts that mutate state outside the mission timeline) that break replay and audit.

Example mission with approval gates

Consider a cross‑service dependency upgrade:

1. Planner scopes impact and creates an upgrade plan with safety checks.
2. Executor updates files, runs tests, and opens PRs with templated descriptions.
3. Reviewer verifies performance and security baselines.
4. Approval gate requests sign‑off in Slack for any service with risky changes. /docs
5. Merge and monitor rollout with clear rollback steps.

Approvals are not speed bumps when routed and recorded correctly. GitHub remains the source of truth, while Slack gets the human in the loop quickly. See /docs and /features

Templates and how to start in agyn

agyn provides mission templates that encode roles, steps, and approval gates so teams don’t reinvent the wheel. Templates keep policy, orchestration, and observability consistent across teams while allowing local variation.

Getting started:

• Choose a mission with clear boundaries (docs/tests refactor, library upgrade).
• Define artifacts and gates up front; pin models and toolchains.
• Turn on process intelligence to route specialist steps and surface insights. /features
• Monitor SLOs for plan accuracy, PR acceptance rate, and time‑to‑merge. /docs

Metrics that actually inform decisions

Measure at the mission and step level rather than globally:

• Plan quality: percentage of steps that executed without re‑planning; number of reviewer‑requested changes per mission.
• Execution reliability: test pass rate and CI flake rate per repo; diff churn before merge.
• Review efficiency: time to first review, approval throughput by team, and exceptions triggered.

These metrics let leaders tune the system (e.g., add a specialist reviewer for migrations) instead of debating anecdotes.

Scaling patterns across teams

Once a few missions are reliable, scale with discipline:

• Template governance: treat mission templates as code with owners, versioning, and change logs.
• Portfolio view: surface throughput, success rates, and policy exceptions across teams in mission control.
• Risk‑tiered autonomy: grant higher autonomy to missions with proven reliability and strong tests; keep high‑risk areas gated.

A small core platform team can maintain the orchestration, governance, and observability surfaces while product teams contribute domain templates.

Security and compliance considerations

Multi‑agent systems need least‑privilege defaults and auditable trails. Practical steps:

• Per‑mission credentials and environment scoping to prevent cross‑mission leakage.
• Secret redaction in timelines while retaining enough context for replay and forensics.
• Policy versioning so audits can tie a decision to the exact rule in force at the time.

Adoption path: from pilot to portfolio

An effective adoption path looks like this:

1. Pilot a low‑risk mission in one repo; iterate until review throughput and pass rates stabilize.
2. Add an approval gate and a specialist reviewer where needed; bake both into the template.
3. Expand to two more repos; set SLOs for plan accuracy and PR acceptance rate.
4. Publish a small mission library; establish an owner and review cadence.
5. Instrument a portfolio dashboard in mission control and use it in weekly ops reviews.

Case vignette: cross‑repo refactor without chaos

A fintech scaled a multi‑agent mission to migrate a logging library across 18 services. Early attempts with a single agent generated noisy PRs and blocked reviewers. By adopting a planner/executor/reviewer pattern with a specialist reviewer for performance‑sensitive services, adding an approval gate for schema‑touching changes, and capping concurrency to two PRs per repo, they maintained steady throughput for two weeks. Mission control timelines showed where plans needed updates, and the portfolio view tracked PR acceptance rate by team. The result: a 30% reduction in total lead time and near‑zero reverts, with clear audit trails for every approval.

Conclusion: make coordination a product surface

Multi‑agent orchestration succeeds when coordination is treated as a first‑class product surface. Define roles and artifacts explicitly, codify handoffs with owners and timeouts, and make SLAs and approvals visible in mission control. Keep context bounded so prompts stay stable and agents don’t step on each other. Then measure outcomes at the mission and step level and evolve templates the same way you evolve services.

When orchestration, governance, and observability work together, leaders get predictable throughput and engineers get fewer surprises. The patterns are repeatable, the risks are gated, and the system improves with every mission.

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Want concrete starting points? Explore mission templates and orchestration patterns that work out of the box. Explore mission templates — visit /features.