01 / THE PROBLEM

Agentic AI does not fail at the model boundary.

It fails in the seams between memory, tools, data, policy, recovery, and ownership.

02 / THE ARCHITECTURE

Make the control plane explicit.

Route each request through defined authority, state, evaluation, and operational boundaries before it reaches production.

03 / THE WORKFLOW

Design for the path after the happy path.

Checkpoint work. Contain effects. Preserve decisions. Make cancellation, replay, and recovery part of the architecture.

04 / THE EVIDENCE

Turn architecture into an argument you can inspect.

Reference builds expose the seams: verified routing, governed memory, durable execution, evaluation, privacy, and change control.

05 / THE OUTCOME

Move past the demo without hiding uncertainty.

One consequential production decision at a time—bounded by evidence, operating conditions, and a clear owner.

Review one production decision

THE PRACTICE

Production AI is a systems problem.

The model is one component. The product is the surrounding harness: the contracts, controls, state, feedback, and operating evidence that let a team trust what happens next.

11: inspectable reference builds
20+: years working across the stack
3: bounded ways to engage
1: production question at a time

01 Make the production decisionReconstruct what the system actually does, test consequential trajectories, and identify what must change before release. 02 Make execution recoverableDesign checkpoints, cancellation, idempotent effects, replay, and operational evidence for work that outlives a process. 03 Make enterprise data AI-readableDefine semantics, contracts, provenance, quality controls, and lifecycle ownership around one consequential use case.

SELECTED EVIDENCE

A reference build is an argument you can inspect.

All 11 builds ↗

Diagram showing verified-capability routing between agents

Featured reference buildVerified-capability routing for multi-agent systemsDynamic agent selection based on verified capabilities and trust boundaries, not marketing claims.Read the case study ↗

PLATFORM / 01Agent sandbox sized to blast radiusIsolation, capability-granted mounts, egress controls, and reconstructable audit trails. EVALUATION / 02Evaluation and observability harnessA typed run ledger for traces, replay fixtures, eval gates, drift signals, and cost attribution. DATA / 03Sutra Feed GuardDeterministic drift classification and governed change before production data is touched.

THE DIGITAL GARDEN

Start with the system boundary—not the archive.

Follow a production symptom into architecture notes, reference builds, and the evidence behind each decision.

Browse the complete garden ↗

01Our AI demo works, but production is stuck → 02Our agent is too unreliable to operate → 03We cannot trust what the agent remembers → 04Our enterprise data is not ready for AI use → 05Our agents have too much ambient authority → 06Our RAG system is stale or impossible to explain →

ONE PRODUCTION QUESTION. A BOUNDED DECISION. INSPECTABLE EVIDENCE.

Design the AI system your enterprise can trust.

Request an architecture review View reference architecture Read the technical garden