AI Reliability: playbooks & guides

A balance scale tipping toward one wax-sealed document over a tall stack of generated sheets: trusted output outweighs abundant generated output

Quality Harness Engineering

Most AI Discussions Ignore the Cost of Verification

There is a number most CTOs cannot explain. Output volume is up, adoption is healthy, and the delivery metric that was supposed to move has not. The productivity did not disappear. It relocated.

A printed QA review scorecard, hand-annotated in pen: a test-count column trending up beside an escaped-defects column that stays flat or worse release over release.

Role Playbooks

AI Doesn't Replace QA. It Forces QA to Evolve Faster Than Any Other Role

Test count is up, the suite runs in minutes, coverage climbed after the AI generators went in. And escaped defects have not moved, or drifted the wrong way. More tests, same protection.

A printed product-flow diagram: a USER INPUT card, an arrow to a PROVEN OUTCOME card, and greyed-out setup steps (connect sources, configure, invite team) pushed aside.

AI Strategy

Your AI MVP Doesn't Need More Features. It Needs One Proven Outcome

Most AI products do not stall for lack of features. They stall because they ask users to build the machine before it does anything, then never check whether anyone comes back. The fix is an outcome-first shape: one valuable result, proven.

A delivery pipeline shown as open gateway checkpoints in cool slate and muted blue, with change flowing through each labelled control stage rather than stopping at a wall

AI governance

AI Engineering Governance Without Killing Speed

You funded the AI rollout. Per-developer output climbed, the demos got faster, and then the delivery numbers refused to move. The slowdown most teams blame on governance is usually the wrong governance, or its absence. Here is the version of the argument that holds up.

A row of printed AI eval test-case documents seen from above on a desk, one sheet annotated and dated, a freshly renewed sheet entering the frame beside it

AI Strategy

Your Eval Set Is a Depreciating Asset

A passing eval suite feels like coverage. It is coverage of the world on the day you wrote it, and that world has already moved. What keeps the suite honest is a renewal loop, not a better set written once.

Printed one-page AI-ready Definition of Done acceptance standard with five named evidence clauses, two rows hand-checked in orange ink on a delivery lead's desk

AI in Software Delivery

Your Definition of Done Still Assumes a Human Wrote the Code

AI output volume is up and the dashboard says adoption works. Escaped defects and rework say otherwise. The gap is a standard your team never rewrote: what "done" certifies once an agent wrote the code.

Editorial decision matrix mapping three agent-architecture axes against five binding constraints, with the Scale column marked as the binding constraint

AI Strategy

Agent Architecture Is a Constraint Problem, Not a Technology Choice

MCP is dead. A2A is the future. Everything should be a workflow. Most of these debates are category errors. The tools are not competing. They manage different constraints, and the real decision is which trade-offs your organization can afford.

An eval-set binder labeled "EVAL SET v3.2" stands foregrounded on a walnut bookshelf beside a recessed PRD ring-binder labeled "PRODUCT REQUIREMENTS v1.0".

AI Strategy

Whoever Writes the Eval Owns the Product

The eval set is not a quality artifact. It is the operative specification of an AI product. Whoever curates the failure cases makes the product decisions, regardless of what the PRD says.

Three-panel editorial dashboard under an OUTPUT DISTRIBUTION MANAGEMENT header: histogram with hallucination tail, weekly trend chart marked DRIFT, and output-channel map with a LEAKAGE…

AI Strategy

Hallucination, drift, and leakage are the same failure in different clothes

The demo works and the eval suite passes. Then production is always one more sprint away - because every AI fix breaks something else. That pattern has structure, and it is not about the model.

Four documents in a row labeling the four product decisions that set an AI feature's cost floor: Scope, Trust calibration, Retrieval, and Latency, joined by a line signaling interdependence.

AI Strategy

Cost discipline for AI products: token economics that do not bleed

Cost becomes a product problem before most teams notice the shift. By the time someone runs the unit economics on an AI feature, the design decisions that drove them are already shipped.

A dark-mode AI evaluation dashboard on a monitor showing a case table with verdict columns, red FAIL indicators, a rising pass-rate sparkline, and a commit-history sidebar

AI Strategy

From AI prototype to production product: the eval-driven path

Every prototype demoed beautifully. Six months later nothing has shipped, and "our AI strategy is basically a list of pilots" stops being a confession and starts being a diagnosis. Here are the eight stages that move a prototype into production.

Quality Harness Engineering

Quality Harness Engineering: The Emerging Stack for Reliable AI Systems