One Price Gap, Two Decisions
A steep price gap makes last quarter's rejected AI projects worth revisiting.
Read briefing →Rational Agent Editorial
AI/LLM Briefing
Evidence-first analysis of material AI developments and what operators should do next.
Hold Agents to the Vendor's Own Bar
What the vendor won't ship tells you what to require.
Read briefing →Exploit Capability Became a Download
Near-frontier exploit capability is downloadable, so patch latency is the control.
Read briefing →What to Write Before Your Agent Connects
A one-page note changes who is responsible for agent actions.
Read briefing →Who Answers for Agent Actions
When agents act on your directions, the record is yours.
Read briefing →One Run Is Not a Result
Run your agent suite repeatedly before trusting any score change.
Read briefing →When Your AI Vendor Gets Investigated
Government probes change what your AI contract must cover.
Read briefing →Why Sonnet Five Five Needs a Migration Plan
Upgrading your model ID requires a migration, not a config edit.
Read briefing →What To Demand Before Agents Get Access
Agent tool access now has a concrete, verifiable standard.
Read briefing →The Stop Rule Your Agent Program Is Missing
OpenAI had monitoring; it lacked the rule that stops runs.
Read briefing →What Agent Logs Your Vendor Cannot Produce
Disclosure improved; completeness is still an open question.
Read briefing →Ask Who Verifies Your AI Vendor
Add one new block to your next AI vendor review.
Read briefing →What Your Signed AI Contract Actually Delivers
The compute you contracted for may not arrive for years.
Read briefing →Agent Test Escapes Now Have Outside Victims
Containment at external evaluators is now an engineering question.
Read briefing →Your AI Tool Inherits Someone Else's Safeguards
The same model now ships with different safeguards and data rules.
Read briefing →Licenses Do Not Create Adoption
Structure drives enterprise AI use, not seat counts.
Read briefing →Measure Agent Work Before Scaling It
Get a baseline before you approve any agent expansion.
Read briefing →Four AI Contract Terms That Cannot Wait
Add them at renewal while AI rules remain unsettled.
Read briefing →Astra's Assurance Case Needs Review
Vendor evasion tests demand executive review before enabling Astra.
Read briefing →What Passes the Agent Promotion Gate
A clever demo becomes a team asset only after six checks.
Read briefing →The Approval Screen Has Moved
Auto mode hands approval to a classifier, not your team.
Read briefing →Safety Posture Is Now a Supplier Question
Gated access and pacing claims now belong in due diligence.
Read briefing →Keep a Private Test Folder
Score new tool versions against your own saved examples first.
Read briefing →Ask What the Score Hides
A benchmark score without its evaluation conditions is not procurement evidence.
Read briefing →Reprice Agent Workloads Now
Cache pricing and retention controls each require a separate engineering review.
Read briefing →Iterate Before You Delegate
Make review the default until a task proves it can run alone.
Read briefing →Put Offensive AI on the Risk Register
Offensive AI is now a present input for enterprise risk planning.
Read briefing →Unresolved Alerts Should Stop the Run
Alerts without ownership, deadlines, and pause rules are not controls.
Read briefing →When Cheaper AI Costs You More
Falling unit costs change which workflows clear the investment bar.
Read briefing →Embedded Model Access Is a Vendor Dependency
Hidden supplier contracts can put critical AI workflows on a deadline.
Read briefing →Equipment Procurement Needs an Agent-Control Column
One new column changes what you can do with hardware later.
Read briefing →The Browser Agent Already Has Your Login
Claude can now act inside your systems using existing employee credentials.
Read briefing →What ChatGPT Ads Mean for Your Brand
Before a competitor tests this channel, your company needs a documented decision.
Read briefing →Your Patch Pipeline Is the Bottleneck Now
AI finds vulnerabilities faster than teams can validate and fix them.
Read briefing →Your Team's First Shared Agent
Start with one workflow, one owner, and a plan to check it.
Read briefing →NIST Wants Buyer Experience on Record
NIST wants AI buyers to comment before the windows close.
Read briefing →Abort Rewinds the Transcript, Not the Cache
A logical rollback that edits the visible transcript while reusing a retained session handle can leave a rejected branch in the state the model actually reads.
Read briefing →What AI Watermarks Actually Prove
Anthropic's new Claude models carry an invisible text watermark worldwide, a signal of the likelihood that AI was involved rather than proof of authorship, and buyers must decide what their content workflows and approval records do with it.
Read briefing →Opposite Bets on AI Data Retention
OpenAI and Anthropic have reached opposite conclusions about whether vendors must hold customer content to monitor for safety, and that split now determines which frontier models regulated workflows can use.
Read briefing →Refusal Behavior Is Now a Procurement Decision
OpenAI sells the same model with different refusal behavior depending on the access tier a buyer purchases, so security teams must evaluate and contract for the exact configuration they will run.
Read briefing →Agents Don't Coordinate Themselves
When agents share writable resources and hold incompatible goals, coordination has to be enforced from outside rather than expected to emerge from the models.
Read briefing →AI Value Is Not a Usage Metric
Usage data can surface promising workflows, but AI investment should be measured by accepted outcomes, full cost, quality, and risk.
Read briefing →The Infrastructure Problem with Agent Authority
Evaluation failures show why agent scope must be enforced at the infrastructure layer, not entrusted to prompts, policy text, or operator intent.
Read briefing →Treat Agent Tests as Production Systems
Cyber evaluation failures show why agent testing needs hard limits, live monitoring, scoped targets, and rehearsed shutdown procedures.
Read briefing →When Evaluations Cross Their Intended Lines
A model evaluation that escaped its intended boundary showed why agent testing needs production-grade containment, identity controls, monitoring, and incident response.
Read briefing →Agents Need Authority, Not Just a Bigger Model
Useful agents require bounded work, explicit authority, evaluation, escalation paths, and reversible operations, not merely access to a more capable model.
Read briefing →