001 The Summary
Telemetry is Jetpack Labs’ production AI orchestration engine: a platform that coordinates 10+ specialized agents across complex, multi-step workflows. Article writers, social media managers, SEO analysts, PR specialists, and sales agents operate in concert, each executing their domain logic within a shared orchestration framework that enforces guardrails, tracks LLM costs per run, and delivers results back to the UI in real time as agents work.
The patterns proven here have shipped in client engagements: 15% logistics cost savings in route optimization, 20% sales conversion lift in marketing automation, and quote turnaround compressed from days to hours in a manufacturing quoting engine.
002 The Client
Jetpack Labs built Telemetry in-house because we hold ourselves to a simple standard: we don’t recommend architecture we haven’t run in production. Every agent coordination pattern, cost-tracking approach, and guardrail strategy in Telemetry has been tested under real load before it reaches a client engagement.
003 The Challenge
Designing multi-agent AI systems without a real production reference is risky. Theoretical architectures often break down at the seams: agent coordination fails under load, safety guardrails get bypassed, costs spiral without visibility, and knowledge retrieval produces stale or irrelevant context.
Key challenges included:
- How to route tasks intelligently across 10+ specialized agents
- How to process multi-step workflows asynchronously without data loss
- How to enforce per-workspace guardrails in a multi-tenant environment
- How to integrate RAG for contextual knowledge retrieval at scale
- How to track LLM API costs per run for operational transparency
004 The Approach
Jetpack built Telemetry as a first-class product, not an internal prototype. The architecture centers on an extensible agent framework where each bot type inherits base orchestration behavior and specializes only its domain logic.
Real-time async job queues power the processing pipeline, with Laravel Reverb (WebSocket) providing live UI updates as agents work. A knowledge base and RAG layer allow agents to pull contextual information at generation time. Role-based guardrails validate agent capabilities before dispatch, catching errors before they reach the LLM.
005 The Solution
Key improvements included:
- Multi-agent orchestration engine supporting 10+ specialized bot types
- Real-time Livewire UI with WebSocket-driven status updates
- Async content generation pipeline with Redis-backed job queues
- AI image generation system integrated alongside text agents
- Knowledge base retrieval via RAG for contextual accuracy
- Workspace management with multi-tenant isolation
- Comprehensive agent architecture documentation for client reuse
Technology: Laravel, Livewire Volt/Flux, Laravel Reverb (WebSocket), Claude API, OpenAI (image generation), MySQL, Redis, Docker, TypeScript, Vite, PHPUnit.
006 The Result
- 95% of potential agent errors caught before reaching the LLM via pre-dispatch validation
- 15% logistics cost savings per route demonstrated in client deployments using the async pipeline pattern
- 20% sales conversion increase in client marketing automation using the multi-agent dispatch pattern
- Quote turnaround reduced from days to hours in manufacturing quoting engine built on Telemetry patterns
- Reference architecture now accelerates every AI client engagement at Jetpack Labs
Telemetry is the proof behind every AI engagement Jetpack Labs takes on. Every agent coordination pattern, guardrail strategy, and cost-tracking approach has been battle-tested in a live system before it reaches a client project.