Bringing the control plane up

AI Workforce Control Plane / 00

Run a hundred agents on a base that won't move.

Trimus is the self-hosted control plane for a company of AI agents — a governed workforce where every autonomous action routes back through approvals, budgets, audit, and tenant isolation. The standing system of record for your fleet.

Self-hosted, single binary Bring your own keys 8 execution adapters
Fleet — [tenant] live
support-triage
heartbeat · 60s interval
budget62%
code-migrations
adapter · claude-code (local)
budget88%
billing-reconcile
awaiting human approval
hold
3 agents · 1 gate · RLS isolated audit ✓ every step
The gap
Governed by construction
Per-step audit trails RLS tenant isolation Per-call cost budgets Human approval gates Encrypted vault + key rotation

The gap / 01

Everyone can run one agent. No one can govern a hundred.

Agents multiply faster than the ability to approve, budget, isolate, and audit them.

88% of pilots die before production; ~70% of enterprises can't govern the fleet they already have. Capability isn't the bottleneck — the base layer under the fleet is missing. Task-runners throw the state away in a cloud VM you can't see into; frameworks hand you a library and leave governance as your problem. Neither is something you can stand on.

No inventory No single audit No budget ceiling
AGENTS IN THE ORG — CLIMBING GOVERNED COVERAGE — 30% 2026 THE UNGOVERNED GAP

The two curves diverge. Trimus closes the gap by making governance the foundation the fleet stands on — three struts that can't be pushed out of true.

Fleet size Under governance The ungoverned gap
STRUCTURE

How it works / 02

Three points make a structure.

Model a company of agents the way you'd structure a team — then a background heartbeat keeps the work moving, with every action routed back through a control before any side effect lands.

01 / COMPANIES

Companies

Each tenant is its own company, isolated by Postgres row-level security and an encrypted vault. Your fleet, your keys, your boundary.

02 / AGENTS & ISSUES

Agents & issues

Define agents, assign them issues on a Kanban board, and let them drive the board back with slash-command action verbs. One governed model across eight adapters — the board is both input and API.

03 / HEARTBEAT

Heartbeat

A background loop wakes each agent on its interval, runs it through its adapter, and checks budgets and gates before any side effect lands.

The compounding moat / 03

Five capabilities that reinforce each other.

Every one routed through the same approvals, budgets, and audit. Adapters generate traces → self-evolution sharpens skills → learned capabilities close gaps → federation makes delegation worth doing. A competitor has to reproduce the whole loop, not a single feature.

01

Governed autonomy

Human approvals, per-call budgets, and incident gates sit in the path of execution. Agents reach further precisely because they can be stopped, scoped, and rolled back — governance lives in the control plane, not a policy doc.

ApprovalsBudgetsIncidents
02

8 execution adapters

One governed agent model across hosted LLMs, local Claude Code or Codex, hardened sandboxes, containers, HTTP endpoints, and remote workers — your keys, your infrastructure. No model-vendor or runtime lock-in.

Hosted LLMLocal CLISandboxRemote worker
03

Self-evolution (GEPA)

A native Genetic-Pareto optimizer mines real execution traces, scores outcomes on a quality / cost / fidelity frontier, and ships prompt edits through a human approval gate. The moat is your per-tenant trace data — it widens with usage.

Trace-drivenApproval-gated
04

Capability learning

When a request has no match, a Concierge learns the capability from the human's answers and installs it as a typed primitive — memory, routine, skill, agent, or federation pointer — behind an approval gate. Competence grows through normal use.

ConciergeTyped primitives
05

Federation: A2A + bidirectional MCP

Speaks Google's A2A protocol as a proxy on its agents' behalf — publishing Agent Cards, translating remote tasks to Issues — and runs MCP in both directions, consuming external tool servers and exposing per-company tools as an endpoint. All under the same RLS, auth, and rate-limit middleware. Value compounds as instances connect; the controls travel with the delegation.

A2A peersMCP bidirectionalSame governed path
LOAD TEST

Governed autonomy / 04

Every autonomous affordance routes back through a control.

Three struts hold the fleet upright — approvals, budgets, audit. Remove any one and the stance collapses; hold all three and nothing pushes it out of true. Opaque task-runners can't show you what happened; frameworks hand you the entire governance burden. Trimus ships the one thing neither can — auditable, revocable, tenant-isolated autonomy — as a single self-hosted binary.

Structural readout

All three struts holding · stance true

Hover or tab onto a control below to remove it and test the stance.
Three load-bearing struts trimus · governance
approvals — in path
GO
budgets — cycle cap
62%
audit — per step
100%
Action ledger · billing-reconcile [tenant]
14:02:07/issue.move → in-reviewaudit ✓
14:02:09/cost.charge $0.041 · cap $5.00within
14:02:11/vault.read stripe.keyscoped
14:02:12/refund.issue $1,240.00approval
14:02:12/egress api.unknown-hostblocked

Every line above is a real control in the execution path — not a log written after the fact.

Why now — 2026 / 05

Autonomy cleared the bar. Governance didn't.

Capability crossed the threshold and the plumbing standardized in the same year. Demand moved from "can an agent work?" to "how do we run a hundred safely?" — the exact gap the base layer fills.

88%of AI-agent pilots never reach production — they break on real credentials, data, and compliance.SOURCE — IDC
~1,600agents projected per enterprise, while ~70% cannot govern the ones they already have.SOURCE — IBM projection
12%have a centralized platform to manage agent sprawl; only ~18% keep a complete agent inventory.SOURCE — governance survey
57%of orgs have agents in production by March 2026 — the question is now governance, not capability.SOURCE — industry survey
STANDARDIZED PLUMBING
~97Mmonthly MCP downloads; donated to the Linux Foundation's Agentic AI Foundation.SOURCE — MCP / Linux Foundation
150+organizations running A2A in production, including AWS, Microsoft, Salesforce, SAP, IBM, ServiceNow.SOURCE — A2A adoption
~70%of enterprises cannot govern the agents they already run — the sprawl outran the controls.SOURCE — IBM projection
18%keep a complete agent inventory — most can't answer "what is the fleet doing right now?"SOURCE — governance survey

Trusted by operators of governed fleets / 06

Built for teams that answer to auditors.

Design partners and early adopters across regulated and security-sensitive workloads.

[LOGO — design partner 1]
[LOGO — design partner 2]
[LOGO — design partner 3]
[LOGO — design partner 4]
[LOGO — design partner 5]
[PLACEHOLDER — design-partner pull quote on governed autonomy in production]
[NAME, TITLE — company]

Traction

[PLACEHOLDER — pilots, deployed fleets, agents under management]
[PLACEHOLDER — revenue / ARR & named logos]

The base layer that outlives the cycle / 07

Put the fleet on bedrock.

Deploy the self-hosted control plane, connect your keys and adapters, and run a company of agents with approvals, budgets, and audit on by default. Set in stone, not in a demo. Request access or read the docs.

Contact [PLACEHOLDER — contact email] · Investors: [PLACEHOLDER — raise & round]

Self-hosted, single binarysovereign
Bring your own keys8 adapters
Approvals · budgets · auditdefault-on