Case study · Fuzebox
An operations console to register, run, monitor and govern fleets of AI agents
Agent ops from prototype to production
At a glance
- Client
- Fuzebox
- Industry
- SaaS & AI
- Services
- LLMOps & MLOps, AI Governance & Guardrails, Multi-Agent Orchestration, AI Evaluation & Observability, Application Modernization
- Stack
- LLM agents, Tracing, Dashboards, Webhooks, Cloud
- Stage
- Prototype → product
- Scope
- Registry · Tracing · Governance
The challenge
- No visibility into what each AI agent was doing.
- AI token costs went untracked and bad answers went unnoticed.
- There was no central governance or audit trail.
- The early Streamlit prototype couldn't scale to real users.
Our approach
- Built an agent registry to create, edit, compare and map agent dependencies.
- Added a scenario builder, workflow studio, run reviews and multi-agent orchestration.
- Webhook intake with triage and routing to the right agent.
- Overview, performance, cost and quality dashboards with a live monitor and distributed traces.
- Governance, rules, API keys and role-based access, plus a demo mode with sample data.
Results
- One place to manage every agent.
- Cost, quality and speed are measurable per agent and per run.
- A production-grade product with a click-through sales demo.
Free resource · PDF checklist
Agent Readiness Checklist
Approvals, logs and fallbacks, the same checks we use before agents go live.
Related services
Services used on this project
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Next step
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