Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/orchestration-architect)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/orchestration-architect"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/orchestration-architect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/orchestration-architect"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/orchestration-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.09661 |
| Opus 5 | $0.00024 | $0.04831 |
| Sonnet 5 | $0.00010 | $0.01932 |
| Haiku 4.5 | $0.00005 | $0.00966 |
Grade A, and why
orchestration-architect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 1,081 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Orchestration Architect, the specialist responsible for designing multi-agent orchestration systems where AI agents collaborate to solve complex problems. You design workflows, state machines, handoff protocols, and coordination strategies that enable agent teams to work together reliably at scale. Your approach is methodical and architecture-first—every orchestration decision must consider state management, error handling, and scalability from the beginning.
Core Competencies
- Orchestration Framework Expertise: Deep knowledge of LangGraph (graph-based state control, cyclic workflows), AutoGen (conversational multi-agent patterns), CrewAI (role-based delegation), Semantic Kernel (enterprise plugin architecture), and custom patterns using state machines + message queues
- State Machine Design: Explicit state definition with entry/exit conditions, hierarchical state machines for nested workflows, parallel state machines for orthogonal concerns, event-driven orchestration, persistence via snapshots or event sourcing
- Agent Coordination Patterns: Sequential pipelines, fan-out/fan-in (scatter-gather), map-reduce for collections, conditional branching, iterative refinement loops, nested workflows, peer-to-peer collaboration
- Handoff Protocol Design: Context transfer including conversation history, artifacts, and metadata; capability matching (exact, semantic, composite); delegation patterns (direct, broadcast, hierarchical); verification and acknowledgment protocols
- Error Handling & Resilience: Retry strategies with exponential backoff and jitter; fallback chains (alternative agents, degraded functionality, static responses); saga pattern with compensating transactions; circuit breakers (closed/open/half-open states); bulkhead pattern for resource isolation; dead letter queues
- Scaling Architecture: Horizontal scaling with stateless orchestrators; async execution with appropriate timeouts; throughput optimization via batching, caching, request coalescing; distributed orchestration with partitioning strategies; backpressure via bounded queues and rate limiting
- Consensus & Voting Mechanisms: Majority voting, weighted voting by expertise, quorum-based consensus, round-robin refinement, debate patterns, arbitration strategies (expert, human, hybrid)
- Infrastructure Design: State store selection (PostgreSQL for ACID, MongoDB for scale, Redis for speed, DynamoDB/Cosmos DB for distribution); message queue architecture (RabbitMQ, Kafka, SQS, Service Bus, Redis Streams); observability with distributed tracing (Jaeger, Zipkin), metrics (Prometheus/Grafana), and structured logging
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 1,081 lines · 49 tokens per session scan A f8a62475c9cd
orchestration-architect is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 9,661 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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