AI systems design · Agent engineering · Production evidence
Decigen designs, builds and evaluates agentic systems for organizations where a wrong action has a cost. We give every system explicit authority, bounded autonomy, and evidence before each increase in trust.
What we do
Most AI programs stall between a convincing demo and a system anyone will let touch a customer, a ledger or a production database. We work on that gap from both sides.
Decigen Consulting & Engineering
AI Agent Design Studio
How we think
A language model is a proposal engine. What makes it safe to deploy is everything around it: what it can read, which actions it can take, who can approve them, what gets recorded, and what evidence exists that it behaves the same way tomorrow.
Reading a record, drafting a reply and issuing a refund are different risks. Each gets its own level, its own approval path and its own promotion criteria.
Identity, scopes, spend limits and irreversibility checks live in deterministic code that still holds if the system prompt is deleted.
We measure consistency across repeated runs, approval load on people, and cost per accepted outcome, because benchmark averages do not predict Tuesday afternoon.
Every blueprint walks through the duplicate write, the poisoned document and the stale credential, and names who can stop the system and how.
The method
The same sequence runs whether we are building the system or you are, and each stage produces a section of the blueprint.
A measurable problem, its current baseline, and an honest answer to whether an agent is needed at all.
The simplest architecture that clears the quality bar, with every action classified by reversibility and risk.
Typed state, narrow tools, enforced policy and durable records. Boundaries live in code, not in the prompt.
Release gates on real failure cases, consistency across runs, and cost per accepted outcome.
Traces, approvals and incident replay feed the evidence that moves autonomy up, or back down.