Building your own agents for on-call and incidents?
Agents that can fix your production issues need research-grade architecture around the model, and a platform that scales with your teams and investigations
4 common ways DIY builds break
Where in-house AI for production breaks down
Costs add up quickly and compound over time
Engineering
People cost
Productizing demo agent takes a mix of AI and systems engineers, with a dedicated investment before reliably using it for on-call and incidents
Systems
Compute cost
Model and compute costs climb with every investigation. As volume grows, your subsidized token costs turn into seven digit invoices
Maintenance
Operating cost
Every model release, team expansion, or new use case needs rework to agent architecture, evals, and productionizing the agent
What engineering teams achieve with Resolve AI
“We pull fewer engineers into war rooms, on-call is materially better.”
“I don't need more numbers or more data. What I need is a root cause.”
“It surfaced accurate root causes 72% faster than our teams, integrating cleanly into our existing stack.”
More on AI for production
Build cost calculator
Put in your own numbers and see what it would cost to build and run this in-house
Engineering AI agents for on-call and incidents ebook
The full build-vs-buy decision, piece by piece
6 Pillars of an Agentic Harness needed to run and fix software
Model orchestration, context engineering, causal reasoning, and more
Build vs buy webinar
Resolve AI's Dave Lawson and Ed Li walk through the build-vs-buy decision, the five pieces you need, and the cost of running each stage in-house
Talk to a Resolve AI engineer
We'll talk through what you're building, what it takes to reach real root cause at your scale, and where Resolve AI fits.
Book a demo
