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Overview

ProductAI for production systems

Resolve AI Agents

On-callYour on-call engineers
IncidentsAgent teams investigate
Operational tasksProactively run tasks

Platform

IntegrationsMeet your stack where it is
SecurityEnterprise-grade by default

Build custom agents

Custom agentsAgents for your workflows

See what ResolveAI can do

Browse

All resourcesEvery guide, webinar and report
BlogEngineering notes and product news
Build cost calculatorModel build versus buy
DocsGuides and API reference

Popular resources

Practical guide: Adopting AI agentsEbook · most read this quarter
How Zscaler gets to RCA in minutesWebinar · customer story
The AI ROI playbookEbook · build the business case
AI SRE buyers guideEbook · evaluate the market

Engineering AI agents for on-call and incidents

How to evaluate frontier AI and prove its ROI in production

Company

About usWhy we started Resolve
CareersWe are hiring across engineering
EventsWhere to find us next
NewsPress and announcements

Talk to us

Book a demoSee Resolve on your stack
Contact usQuestions, partnerships, press

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4 primitives for an agent that runs on-call and improves reliability

Thu, Nov 1911:00 AM PST, 30 min

Imagine you are never on call. An agent you trust holds the critical path: it takes the page, investigates, acts in production, and fixes the root problems behind repeat alerts and incidents. Trusting it with that work comes down to four primitives: context, quality, guardrails, and economics.

Join Steven Karis, Chief Architect at ResolveAI, on how to build each primitive into agents that own on-call rotations and improve reliability autonomously.

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Featuring

SK

Steven Karis

Chief Architect at ResolveAI

LinkedIn profile for Steven Karis

In this session, you will learn how to:

Ground agents in live production
Make sure your agent knows what’s happening in the system right now
Build investigations you can rely on
Get accurate answers on every alert and measure quality with evals built from real production incidents
Decide when an agent is ready to act
Define autonomy based on the agent’s track record that lets an agent take action in production on your behalf
Keep costs in check
Manage spend as alert volume grows, and reduce repeat alerts over time

4 primitives for an agent that runs on-call and improves reliability | Resolve AI