Owl AI blog
Writing from the Owl AI team on evaluating and improving user-facing agents.
Agent sessions: evals and shadow clones without hosting the agent (2026-08-21): Owl records the AI agent you already run. Traces, evals, experiments, and shadow clones live in the dashboard. The agent stays in your process. Owl never runs it.
From finding to pull request: what we automate and what we don’t (2026-08-20): Owl opens pull requests for agent fixes. People assume that means we let a model rewrite your agent unsupervised. Here is where the line actually sits, and why it sits there.
Voice agents fail differently (2026-08-13): Everything we thought we knew about measuring chat agents needed adjusting for voice. Barge-in, silence, and the fact that a caller cannot scroll back.
The eval suite that only goes up (2026-08-06): If your eval scores have improved every quarter and never regressed, that is not a sign of a healthy team. It is a sign your test set has become a training set.
How long to run an agent experiment (2026-07-30): Agent experiments have a nasty property: the metric you care about reports slowly and the metric you can see reports fast. A practical approach to stopping rules, without a stats degree.
Agents don’t fail at language. They fail at tool calls. (2026-07-23): Across the failed sessions we have looked at, the model saying something wrong is a minority of the problem. The majority is the model calling something wrong, or not calling it at all.
Why we don’t host your agent (2026-07-16): Most agent platforms want your agent to run on their infrastructure. We decided early that ours would not, and it has cost us some deals. Here is the reasoning.
Reading a bad session, line by line (2026-07-09): A walkthrough of one real failed session — what the transcript shows, what the transcript hides, and the four places we look before forming an opinion.
The prompt change that won every eval and cost us retention (2026-07-02): A design partner shipped a rewrite that improved helpfulness, accuracy, and tone. Seven-day return rate on the affected intents fell 11%. The postmortem is more interesting than the mistake.
The blind spot: how users and agents use your product through API and MCP (2026-06-27): More of your product is reached through APIs and agents every month. Recording tools never see it. Here is why API and MCP capture is the next thing analytics has to get right.
Shadow traffic without shadow infrastructure (2026-06-25): Running a candidate agent against real production traffic usually means standing up a parallel environment. It does not have to. Here is the shape we settled on, and the failure mode we had to design against.
Capture, Analyze, Ship, Measure: the product loop in one platform (2026-06-20): Coding agents made shipping easy. The hard part is knowing what to build. Here is the loop Owl runs to close the gap from behavior to measured outcome.
Ninety seconds is not a universal constant (2026-06-18): Every analytics tool ships a default idle timeout and calls sessions abandoned after it. We looked at the actual return-probability curves across a dozen products. They do not agree with each other, or with the default.
What we actually mean by an outcome scorer (2026-06-11): Outcome-grounded evaluation sounds obvious until you try to define one. Here are the five signals we build scorers from, the traps in each, and why they have to be derived per product.
The eval said 94%. The support queue disagreed. (2026-06-04): Our judge model scored the agent at 94% helpful. Meanwhile a fifth of those same conversations ended with the user opening a support ticket. Here is what we learned about grading answers instead of outcomes.
This is Owl AI at withowl.ai. Not owl.co (insurance), owl-ai.com (courses), aiowl.org, the OwlAIProject repo, or CAMEL-AI OWL (the open-source multi-agent framework).