Runtime context
for your software factory
Hud’s runtime code sensor learns how every function in your backend runs in production. Safe changes merge automatically, the rest reach an engineer with the context to review them, and if one regresses as it takes traffic, your agent gets the evidence to fix it.
Agents know your code.
They’ve never seen it run.
Slow down
Ship blind
Hud offers a third option.
Bridging the gap between coding agents and prod with Runtime Context.
Safe code ships right away, and everything else gets a more detailed review.
Why Hud?
Runtime context from PR
to fully deployed.
The third option takes three things: knowing which
PRs are safe before the merge, watching every rollout,
and having the forensics ready when something slips through.
Hud’s Solution
01 · Before the merge
Today: It waits for a merge or gets approved unread.
The diff shows what changed.
Hud shows what it will touch in production.
The functions it touches, their traffic and their callers.
Then a rating: safe, or needs review. Safe PRs can be
auto-merged or merged on Hud’s recommendation.
How “safe” is decided. A change to low-traffic code within its normal, with no new downstream calls and off the hot paths, is marked safe. A PR Hud can’t rate is never marked safe.
02 · During the rollout
Today: One function quietly regress, and customers find it first.
The current deploy, checked
against the base version.
Every change gets this watch, even ones that skipped review, so skipping the queue doesn’t mean shipping blind.
New kinds of exceptions flagged, even caught ones.
Rolled back through your CD, or after your approval.
monday.com caught a rollout regression 14 minutes in, scoped to the function.
03 · When something slips through
Today: Errors are up, and the logs don’t have the answer.
The agent that writes the fix
needs to see what happened.
Forensics already captured: call graph, request, exception.
Your coding agent gets them over MCP and opens the fix.
Hud counts every call to every function.
Traces only sample your service. Hud’s sensor runs in your backend services, so the data exists before anyone thinks to ask for it.
The Impact Map: a real-time map of how your code behaves in production.
On an error or a spike, captures forensics: call graph, request URI and parameters, exception and stack trace.
Runs in your pod. A PII scrubber removes 50+ sensitive keys, plus emails, tokens and card numbers.
Learns a baseline on a separate thread, with bounded buffers. Turns itself off on anomalies.
Keep your stack. Hud adds what it can’t see.
Your APM samples traces and alerts on the services you instrumented. Hud runs alongside it, with nothing to define and no sampling.
AI code reviewers read the diff. Hud adds what the diff can’t show: how the code it touches runs in production.
AI SREs start from the incident. Hud starts from the change, and catches the regression while it’s still rolling out.
Your coding agent knows the repo. Over MCP, it gets production context while it writes, and the forensics when it fixes.
Give your software factory production context.
See Hud in Action
Trusted by engineers.
Human & artificial alike.
Hud runs on millions of services across massive production environments, with negligible overhead.
“Hud eliminated our voodoo incidents. AI-powered root cause analysis turned days of drilling through tools into minutes.”
“Hud completely changed how early we detect, and how quickly we solve issues long before they impact the business.”
“AI helps us move faster only when it’s grounded in reality. Having accurate runtime context means our engineers, and our AI workflows, can reason about real production behavior and act on it safely.”
“We knew the crashes were caused by an ELU spike, but the question was what caused that spike. Using Hud, we found the answer in minutes with an AI-ready fix.”
“When engineering decisions are grounded in real execution data, it changes how confidently we ship, scale, and invest. That level of certainty directly supports the business as the platform continues to grow.”
