Engineering & Strategy
AI adoption needs a budget for variability
Output variation changes review demand, release risk, and the cost of scaling AI. Measure those consequences before expanding adoption.
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AI adoption needs a budget for variability
Output variation changes review demand, release risk, and the cost of scaling AI. Measure those consequences before expanding adoption.
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Agent security
Agent monitoring needs a tested stop path
New monitoring disclosures highlight an operational question: after an unsafe action is detected, can the system actually stop the work?
Software assurance
AI-generated proofs need a precise contract
New formal-verification research makes a stronger case for machine-checked evidence—and for reviewing exactly what that evidence guarantees.
Critical operations
Legacy modernization needs behavioral evidence
Better AI performance on older languages opens useful possibilities. A migration still needs evidence that business behavior survives the rewrite.
AI Engineering
Reliable agents need operating boundaries
A capable model is one part of a dependable system. The harder work is defining what success, recovery, and a safe stop actually mean.
Quality & Evaluation
Evaluate the work, not just the answer
An AI evaluation should tell you whether a business task succeeded, where it failed, and whether a change is safe to release.
Security
MCP security begins with trust boundaries
Connecting an agent to tools is easy to demonstrate. Making those connections accountable requires careful identity, authorization, and isolation.
Software Delivery
Coding agents change the review bottleneck
When producing code becomes faster, trustworthy review depends on smaller changes, clearer evidence, and attention to consequential decisions.
Data & Architecture
Retrieval must respect the permission model
A useful answer is only trustworthy when every source behind it was available to the person asking the question.
Human Systems
Human approval is a product design problem
A confirmation button provides little protection if the person cannot understand the action, its evidence, or what will happen next.
Reliability
Observe AI workflows as business operations
Token counts and response times are useful signals. They become actionable when connected to task outcomes, dependencies, and human recovery.
AI Economics
Model routing should optimize cost per outcome
The cheapest model call can create the most expensive workflow when retries, review, and incorrect actions are included.
Governance
AI governance needs evidence from the system
Policies become useful when teams can show how a real AI workflow is owned, evaluated, authorized, and changed.
Architecture
Durable workflows are the foundation for long-running AI
An agent that waits, retries, and touches external systems needs durable state and explicit recovery semantics as much as it needs reasoning.
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