Use case · Engineering
Your engineers' coding agents are the most capable tools you have bought, and the bill shows it. Most of that spend is re-derivation: a thousand agents working out the same fix, the same gotcha, the same setup, from scratch, every week. Firmament captures the lesson once, proves it against what actually shipped, and serves it to every agent on the team.
First run · Claude Opus 4.8 · no memory
≈ $6.40
figures it out: full reasoning, retries, dead ends
the lesson is captured and stored in Firmament
Every run after · DeepSeek v4 Flash + the lesson
≈ $0.70
or Claude Haiku 4.5, or GPT-5.1 Codex Mini: whatever is cheap that quarter
same task · any vendor
11% of the costCoding-agent spend is the runaway cost of the moment, and it climbs with adoption, not despite it. The reason is simple: agents bill by the work they do, and most of that work is figuring out something a teammate's agent already figured out. The fix is not a cheaper model or a usage cap. It is making sure the expensive thinking only happens once.
“It's very hard to draw a line between one of those stats and, okay, now we're actually producing like 25% more useful consumer features.”
Andrew Macdonald, President & COO, Uber
After burning through its 2026 AI budget in roughly four months, Uber capped employees at $1,500 a month per agentic coding tool. TechCrunch, Jun 2026
Never retry Stripe webhooks by hand
payments · from Maya's agent
Gate payment deploys on migrate-check
platform · from Devon's agent
Retry flaky S3 uploads with backoff
backend · from the CI agent
Never bump the ORM without the lockfile
company · written by Priya
this week
0 agents serving these lessons · 6 teams
Figuring a task out for the first time needs a cutting-edge model: full reasoning, retries, dead ends. Doing it again does not. Firmament stores the lesson the first run produces, so every run after can take a much cheaper model and still get it right. The first agent pays for the discovery; the rest of the team gets it served.
The fix your strongest engineer's agent found this morning guides everyone else's this afternoon. No more parallel teams burning tokens on the same wrong turns, in different repos, in different tools.
An agent that already knows your build setup, your conventions, and what to never do executes instead of exploring. Shorter task horizons, fewer retries, less human babysitting, and a smaller bill for the same result.
One hosted MCP URL connects the agents your engineers already use. The knowledge follows the engineer across tools and repos, and it stays with the company, not locked inside one vendor's memory.
Why it starts with engineering
Engineering is where a shared brain earns trust, because the outcomes have a referee. A rule like "run migrations before deploying payments" gets reinforced every time it holds in CI and a real deploy, and retired the moment an outcome contradicts it. The knowledge carries a track record, not just an author.
Other teams