Observability is dead. Long live outcome engineering. You and your AI agents write code. Firetiger makes sure it works.
San Francisco
Joined August 2024
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Firetiger retweeted
The most advanced use of @usefiretiger I've seen has come from @hyperspell
Firetiger retweeted
driving continuous improvement loops in software systems is really hard, even if you have really smart agents (codex, claude, cursor), really smart humans, decent observability tools, etc.
today we're launching Service Monitors, loops in prod made easy. blog.firetiger.com/firetiger…
Nobody actually cares about slow queries.
They care that checkout is broken.
Firetiger agents start from the customer impact, then trace issues back to the technical root cause.
#OutcomeEngineering #AgenticAI #DevOps
Shipping code faster is easier than running it safely in production.
@usekernel is building infrastructure for AI agents, which means things move quickly, and reliability matters a lot.
In this post, the Kernel team explains how they use Firetiger to monitor deployments, catch issues early, and reduce manual investigation during incidents.
blog.firetiger.com/how-kerne…
Firetiger retweeted
effective LLM prompt caching makes or breaks inference budgets. tuning cache TTLs is hard and fiddly.
we pointed persistent background agents at the task and saw a 77% reduction in cache write waste in a week.
more on how here: blog.firetiger.com/agentical…
Firetiger retweeted
@TownAI only had to connect @axiomhq and @convex to their @usefiretiger agents to get high-quality change monitors setup.
Now an agent joins each of their PRs, drafting a monitoring plan for what to lookout for when changes get deployed, and reporting with RCA on every issue.
Until you have agents in production, your haven't closed the SDLC loop, this is how you do it ↓
“We pay close attention to every Firetiger notifications.”
🫳🎤
It's been amazing hearing from engineers like @georgekontridze from @TownAI about how they've adopted @usefiretiger
Everyone is using AI to write code now.
So what breaks next?
- Code review
- Trust
- Observability
In EP02 of Canned Responses, @lalkaka invited @templaedhel (CTO @ Metronome / Stripe) to taste tinned fish and talk about the future of engineering.
Firetiger retweeted
a fun new feature! "fun", perhaps. defining and tracking Service Level Objectives are never fun! UNTIL TODAY!
@usefiretiger agents now identify good service level indicators and set objectives intelligently when you set them forth on a mission to improve an outcome in production.
every eng leader I talk to wishes they had a more mature SLO program, but there are varying levels of skill in orgs and driving broad adoption via humans is really hard. just another way agents are upending the way we as an industry build and ship software!
tell a Firetiger agent "monitor for user-impacting errors" and it will figure out the right metrics to track, set targets, and evaluate them every session. No SLI selection meetings. No dashboard wiring. No stalled SLO initiatives that never make it past the planning doc.
the best part: when the agent finds an issue, it triages it against those SLOs automatically. Instead of "here are three problems, good luck, have not so much fun" you get "this one is actively impacting 6 customers and degrading a metric you said you care about. Fix this first."
we wrote up how it works and why we built it on the blog, link BELOW!
Firetiger retweeted
A few weeks ago, @lalkaka threw this a new idea out:
“Let’s interview tech leaders… while eating canned fish.”
You think this was a joke, it isn’t, and everyone we talked to about it immediately loved the idea!
So we filmed it and it’s called Canned Responses. Episode 01 is out with @lukerramsden from @tryarchitect.
That’s Rustam’s secret sauce. He’ll start with something slightly unhinged… and somehow it lands exactly where it should.
Hope you all enjoy watching it as much as we enjoyed making it!
#TechTalk #AIEngineering #AIInfrastructure
Firetiger retweeted
we run into the same compaction bug and have the same hack in our agent harness @usefiretiger .
fun to see the emperor has the same hole in their clothes.
alex000kim.com/posts/2026-03…
Large tool outputs quietly kill agent performance.
You call an API, pass everything to the model, and hope for a clean answer. You get timeouts or messy reasoning instead.
@swnelson_ from Firetiger breaks down how we handle it:
- Truncation, which appears simple at first (but is full of subtleties).
- Saved artifacts, which we highly recommend, if you have the infrastructure for it.
Short read, real examples:
blog.firetiger.com/agent-eng…
#SoftwareEngineering #OutcomeEngineering #AgenticAI
We’re shipping more software than ever.
Agents are writing code, pushing changes nonstop.
But customers still feel sad when things break.
That’s where Firetiger comes from.
#OutcomeEngineering #AgenticAI #SoftwareEngineering
If agents are writing code and shipping changes, they need a different kind of data layer than one designed to render metrics on a dashboard.
Firetiger Founding Engineer, @JohnPugliesi , just dropped a great piece on something we’ve been thinking a lot about at Firetiger:
blog.firetiger.com/we-built-…
#OutcomeEngineering
AI agents now write all our code but when software breaks in production, customers are still knocking on your door.
Why?
Because observability tools measure symptoms, not outcomes.
@lalkaka and @__Achille__ explain why we think the future is Outcome Engineering.
Watch ↓
#AgenticAI #OutcomeEngineering
