Build agents that earn their place.
Agentrify is a platform for building multi-model, multi-agent systems, testing them against real work, and improving them continuously against business value, with every change approved by a person.
Most agents are deployed once, then left alone.
Organisations are putting agents into production faster than they can test, improve or measure them. Performance drifts, costs creep up, and nobody can say what each agent contributes to the accounts.
Built on one model
Teams lock into a single provider, overpaying for simple work and lacking options when a better or cheaper model arrives.
Never systematically improved
Improvements happen by hand, without test suites, version control or a record of why a change was made.
Measured by activity, not value
Dashboards count tasks and tokens. Boards want to know which line in the accounts each agent moves.
Four parts, one loop.
Each part works on its own; together they form a governed improvement loop for every agent you run.
Studio
Design multi-agent systems and route each task to the right model, from several providers.
Explore →Evals
Test agents against real cases, edge cases and policy rules before and after every change.
Explore →Evolve
Generate candidate improvements, test them, and deploy only what a named owner approves.
Explore →Value
Tie each agent to the business line it should move, using the Agent Value Ledger method.
Explore →See a governed improvement cycle.
Run the loop on a sample finance agent. Each cycle proposes a change, tests it on 500 cases, checks it against policy and the Delegation Map, and waits for a person to approve. One candidate tries to expand the agent's own authority. Watch what happens.
Live agent
Version history
Improvement cycle
A simplified demonstration of how Agentrify Evolve is designed to work. Figures are illustrative, not results from a live system. Ledger value assumes 10,000 cases a month at a manual cost of S$9 per case.
Agents improve. People decide.
Continuous improvement is only valuable if it is safe. Agentrify is built so that agents get better over time, but never on their own authority.
Human approval gates
Every change to a production agent is tested, explained and approved by its named owner before it goes live.
Versioned, with instant rollback
Every version is recorded with its evidence. Any accountable person can roll back in one step.
No self-promotion
An agent can never widen its own authority. Moving a decision outward needs evidence and owner approval through the Delegation Map.
For the people who answer for agents, and the people who build them.
Executives, boards and operations heads
See what every agent is worth, approve how it changes, and show regulators and auditors a complete record.
- Value tracked against the accounts
- Approval of changes, not just of launch
- Evidence ready for assurance
Engineering and AI platform teams
Build with several models and agent patterns, test properly, and ship improvements without fear of breaking production.
- Multi-model routing and fallback
- Evaluation suites in the pipeline
- Versioning, rollback and audit by default
One approach, three disciplines.
Agentrify is designed to work alongside two related ventures founded by Dr. Elias Kairos Chen. Each can be used on its own.
Decides what agents should do, and what each one is worth.
Builds, tests and improves agents against business value.
Polices every agent and proves what it did.
Shape the platform with us.
We are working with a small number of organisations and engineering teams to finalise Agentrify before release.