For startups & scale-ups
Your agents stop making the same mistake twice.
The limit on autonomy isn't intelligence, it's briefing. An agent that knows your rules and your context can carry a task much further before it needs a human, on a much cheaper model, and every user on the team gets that extra performance.
Works with any agent that speaks MCP
Day one
Bring your CLAUDE.md. Skip the cold start.
Import the rules files your team already maintains: CLAUDE.md, AGENTS.md, Cursor rules. They distill into wiki pages you can edit, and your whole team's agents share one brain from the first afternoon.
- One MCP URL per workspace, your existing login.
- Works in every tool your team actually uses.
- Ready in five minutes, no pipeline changes.
- platform
Deploy process
from CLAUDE.md · 6 claims
- engineering
Code review conventions
from CLAUDE.md · 4 claims
- payments
Payments service
from architecture.md · 9 claims
stylized · 3 pages from 2 files · edit or remove any
Open your wikiHow it works
Ready in five minutes.
One MCP URL, your existing login, no pipeline changes. Access is user-scoped: every agent sees exactly what its human is allowed to see, nothing more. The learning is Firmament's job, server-side, in the background.
01 · ask
Before a task, the agent pulls your org's proven guidance. Plain language in, proven guidance out.
02 · submit
After the task, it reports what worked and what didn't. That's the whole integration.
03 · onboard
Point your own agent at your docs: it ingests the corpus overnight, on your tokens, and every page lands in your wiki, ready to review.
→ ask("deploying payments, anything I should know?")
← Run make migrate-check first; the smoke test misses schema drift.
team wiki · deploy-process
… task completes, first try …
→ submit("migrate-first worked; added a CI check for drift")
✓ processed → folded into the team wiki
Longer leashes
Let your agents run.
Every session starts pre-briefed, so agents stop stalling on missing context and stop repeating last month's mistakes. Longer task horizons, fewer interruptions, sustainable enough to run all day.
- No more re-explaining the company every session.
- Lessons from one task carry into the next, across every tool.
- Power users get power: the agents of your best people make everyone's better.
Compounding
A mistake fixed once is fixed for everyone.
Lessons are reinforced when they keep passing CI and corrected when reality disagrees. The team's agents climb together, and the knowledge belongs to the company, not to whoever learned it first.
- ↗ 0×
Never retry Stripe webhooks by hand
payments · from Maya's agent
- ↗ 0×
Gate payment deploys on migrate-check
platform · from Devon's agent
- ↗ 0×
Retry flaky S3 uploads with backoff
backend · from the CI agent
- ↗ 0×
Never bump the ORM without the lockfile
platform · written by Priya
this week
0 agents serving these lessons · 6 teams
Cheaper, with control
Autonomy you can afford to leave running.
Lessons learned on the expensive model get served to the cheap one, so an always-on agent stops being a luxury. And you keep the reins: what enters team knowledge is shared by you, scoped by you, and auditable end to end.
- Learn on Opus once; run on DeepSeek forever.
- Review controls, scopes and an audit trail: control is built in.
- See everything your agents learned in one wiki, not per session.
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 cost