Edgedelta

The bottleneck in a software organization has moved from writing code to operating it, and most of operating is quiet upkeep nobody schedules. Loops are standing goals your AI Team keeps verifying, bounded by the guardrails you already set.

We built real user monitoring into our existing product, so a slow page load and the backend request behind it land together as a single thing to investigate.

Every prompt our agents send passes through a redaction layer that strips secrets but keeps their fingerprints, so investigations still work and model providers never see credentials.

A 10 kB script collects page loads, route changes, page speed scores, request timings, and JavaScript errors from real browsers, then sends them into the pipeline we already run. It replaced Sentry in our own web app.

How we made a machine-built map of production trustworthy enough for agents to bet an incident response on it, from expiring stale facts to pointing the blast radius query in the right direction.

A trust level is a promise that every individual tool has to keep. This post covers how thousands of per-tool decisions get made, and how you audit the result.

Benchmarking the whole agent, not the model, on real incidents, and turning resolved cases into a fine-tuned root-cause model that must beat frontier baselines first.

Edge Delta's Knowledge Graph, an automatically maintained map of your services, teams, and incidents, is now queryable directly from the edx CLI, giving your coding agent blast radius and ownership context in the same investigation where it finds the bug.

Putting agents near production raises two fair questions. What do they send to a model while they investigate, and what are they allowed to do once they have the answer? Guardrails answers both, with a ladder you climb one domain at a time and a preview b

Every model is a swappable, cost-aware, fail-safe component, because providers deprecate, refuse, and get embargoed on their timeline, not yours.