Bring any model. Brahmalabs is the substrate where your agents execute: sandboxed runtimes, approval chains, signed audit trails. The same engine extends to every other durable workflow in your business.
The thesis behind Brahmalabs: the service is now delivered as software, by agents. We lead with software engineering — the highest-leverage domain in a technical org — and extend to every other place where work can become workflow.
Read our approachEvery workflow below is built out of these primitives.
Not use cases — the primitives that make them possible. None are bolted on; they are the platform.
Every agent is versioned and promotes through draft, staged, production, with rollback in one click. Skills are private to your organisation by default, moderated before anyone else can use them.
Custom sandbox images match the workload: a browser agent gets a browser image, a data agent gets a data image. Credentials never reach agent code; the vault injects short-lived, scoped references at call time.
Single approver, N-of-M multi-party sign-off, or escalation chains when the first approver is unavailable. Every decision — and every artifact the run produced — attached to the audit log.
Build, test, secure, ship, operate, evolve. Nine specializations, each as a set of durable workflows with approval gates, signed artifacts, and complete change history.
Accessibility regressions and off-spec UI reach production because manual review doesn't scale with PR volume. Generated code runs sandboxed a11y and visual-regression checks, routes to design review, and deploys behind a staged rollout — sign-off logged against the commit that ships.
Token migrations drag on for a quarter because no one can hand-review hundreds of component changes. A refactor agent rewrites consumers against the new tokens, visual-regression catches drift, and the design lead approves batch by batch.
Codesigning keys end up stashed in CI or a shared keychain no one can audit. Here they're pulled from the vault at build time, the device farm runs the suite, release manager and QA both sign, then the store submission goes out.
A production crash costs an engineer the morning reconstructing what changed. The triage agent analyses the stack the moment the signal arrives, correlates it with the suspect commit, and drafts a fix-suggestion PR.
Breaking changes get discovered by downstream teams in production, not in review. When a schema change lands, a breaking-change agent computes consumer impact, runs the contract suite, drafts a migration plan, and gates the deploy on human approval.
An endpoint sunset breaks a downstream team because no one tracked who still calls it. Mark it deprecated and the workflow scans consumers, computes migration windows, notifies owners, and schedules a sunset date with mandatory acknowledgment.
An unreviewed transform on regulated data is a compliance incident waiting to happen. The change runs dry in an isolated image, PII and lineage diffs compare against production, and a data steward approves before it applies — every step captured as evidence.
Bad data reaches dashboards and decisions before anyone notices the drift. Scheduled checks compare today's distributions to baseline, open tickets with the suspect commit attached, and hold any auto-remediation for steward approval.
Manual regression passes take days and gate every release. Describe the flow once in plain English — sign up, navigate, fill forms, verify — and it re-runs on every deploy in a browser image pinned to the version your customers actually use.
Triaging a flaky failure means re-reading logs and re-investigating problems the team has already seen. The agent reads the logs in a minimal CI image, pulls the suspect diff, searches your knowledge base for similar past failures, and drafts a fix linked to the run.
Scanner output is mostly noise, so real findings drown and the security team burns out closing false positives. The workflow escalates only genuine issues with context, attaches exception justifications to the audit log, and closes the rest without paging anyone.
Every published CVE triggers a fire drill, whether or not your code is actually exploitable. The agent assesses real exploitability in your codebase, drafts a compatibility-tested bump PR, and routes to the security team only when it matters.
A multi-step release across migrations, Kubernetes, and smoke tests is exactly where unlogged changes slip in. Each step runs in the right image — database-client, kubectl, your staging image — and release manager and SRE both sign before traffic shifts.
Live cloud state drifts from your declared infrastructure silently — you find out during an incident or an audit. Scheduled agents with read-only credentials diff the two, file the gap as an artifact, and queue remediation PRs for approval before anything applies.
A 3 AM page means on-call manually correlating the last deploy, the logs, and the error signature under pressure. The triage agent does that the moment the alert fires and drafts a status update and a revert PR — high-blast-radius rollbacks still wait for on-call and SRE lead to both sign off.
Postmortems are the chore everyone defers, so they slip for weeks and the action items evaporate. When an incident closes, an agent assembles the timeline from logs, metrics, and chat transcripts, drafts a blameless writeup with proposed action items, and routes it to the SRE lead.
Big refactors never get prioritised because no one can hand-review hundreds of changed files. Refactor agents work in isolated worktree branches, the full test suite verifies every change, and human approval gates the merge.
Framework upgrades sit 'in progress' for years while EOL and security risk pile up. The agent generates an incremental plan and applies it batch by batch in worktree branches — test suite verifying, human approving each merge. Java 8 → 21, React 17 → 19, Rails 5 → 7. Same shape.
Service-as-Software isn't a software-engineering concept. It's the thesis. The same engine your engineers use already runs cross-org workflows where signed accountability matters most.
Tier-1 replies drafted by a versioned agent, personal data stripped before the model sees it. Promote new versions through draft → staged → production; multi-language out of the box.
Every asset checked against brand guidelines, regulated-claim policies, and consent state before publishing. Legal sees only the borderline cases.
Control evidence pulled from the append-only log and stored as stable, versioned artifacts. Vendor-risk reviews and access certifications run on the same primitives. Auditors get URLs, not zip files.
Privileged operations surface as approval messages; manager and IT both sign. Every change in the audit log with the reason attached. Ad-hoc Slack approvals become accountable.
Every one of these is a workflow definition you can fork. Start free, or talk to us about a sovereign deployment.