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Why internal agent projects stall after the first good demo
Dhruv Mahajan, Chief AI Scientist at Resolve AI, on why early results are misleading, what a team runs out of when it tries to improve an agent, and why building evals is a machine learning problem.
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Leading the cost and accuracy curve for production investigation
Dhruv Mahajan, Chief AI Scientist at Resolve AI, on why the whole curve matters rather than any point on it, how you tell a real improvement from a trade, and why the thing being measured is a system rather than a model.
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How the agent learns and improves over time
Noah Schlager, Member of Technical Staff at Resolve AI, on the manual work memory automates, how the agent knows an environment before the first alert fires, and why generating knowledge is easy but useful knowledge is hard.
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Why root cause lives between your tools and systems, not inside any one of them
Swarit Joshipura, Member of Technical Staff at Resolve AI, on the middle-of-the-night page for a service ten hops upstream, why Resolve AI queries data live instead of ingesting it, and what makes observability integrations so hard to build.
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What it takes to let an agent take action in production
Claire Yin, Member of Technical Staff at Resolve AI, on what it takes to let an agent take real action in production, where evals end and guardrails begin, and why there’s no trade-off between safety and efficiency.
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Token efficiency in AI agents: charging for the work, not the tokens
Varun Krovvidi, Product Marketing at Resolve AI, on why token counts became the industry's scoreboard, what a well-run investigation reads and skips, and why Resolve AI bills for completed work instead of consumption.
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Always on agents for daily engineering tasks in production
Justin Smith, Founding Engineer at Resolve AI, on agents that react to events instead of waiting for a human, where teams point them first, and why the hard part is knowing what to do rather than doing it.
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How do you evaluate an AI agent that investigates production systems?
An eval gives an agent a task with a known correct outcome, runs it several times, and grades the result — turning “the feel of the model” into an objective metric. What agent evals are, who grades them, how to build a suite, and why production troubleshooting is the hardest thing to evaluate.
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How Zscaler Engineers Get to RCA in Minutes
Join Chris Umbel (Sr. Principal SRE, Zscaler) and Josh Grose (Head of GTM, Resolve) as they explore how Zscaler integrates AI directly into incident response workflows. Their engineering teams employ AI hundreds of times weekly to maintain shipping velocity while sustaining reliability standards.
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How DoorDash keeps a billion-dollar ads platform resilient in production with AI.
