AI integration and orchestration
Keep the AI feature reliable enough to leave running.
Routing, retrieval and agent workflows built into a product that already exists, with the monitoring that tells you when a model starts being wrong.
the work
What we build into the product.
We run this ourselves in AgenTwo, an agent platform with an interactive harness over an operative agentic layer, so the reference is software we operate rather than a slide.
The moving parts
- Multi-model routing and fallback
- Retrieval and memory over your own data
- Agentic layering: agents that delegate and report
- An interactive harness a person can drive
- Prompt and output validation
What keeps it up
- Monitoring that catches a wrong answer, not just a 500
- Retries, timeouts and graceful degradation
- Cost and latency visible per call
- A path to roll back a model change
why it goes quiet
AI fails differently to software.
Ordinary software breaks loudly. A model keeps answering, just worse, and the monitoring you already own cannot see it.
It drifts
A provider ships a new model version and the same prompt returns something subtly different.
It stalls
Rate limits and slow responses turn a feature that worked in testing into one that times out under load.
It is confidently wrong
The worst failure returns a 200 and a plausible answer. Validation is the only thing that catches it.