Context Clues

We ran a controlled benchmark: eight tasks, forty-one decision points, forty-eight runs. AI coding agents without product context followed team decisions 46% of the time. With context, 95%. The gap is about information access, not model capability.

MCP operates on a client-server model with centralized intelligence. ACP treats agents as first-class citizens in a network, enabling genuine specialization, composable pipelines, and context as a first-class concern.

New research named the exhaustion you feel after reviewing AI code: brain fry. The fix isn't less AI; it's reviewing decisions, not diffs.

Multi-agent workflows amplify the telephone game. The teams that build shared decision infrastructure first will ship faster with fewer regressions.

Your team's product knowledge exists as an interconnected graph of decisions, tradeoffs, and customer signals. You just can't query it.

Your agent shipped perfect code that does the wrong thing. The bug isn't in the implementation—it's in the product decision that was never made clear.

I almost built Brief as a meeting bot that simulated product reviews. That would have copied the surface instead of solving the real problem: context infrastructure.

PM tools fail because they track the how and ignore the why. Without decision infrastructure, they become ledgers instead of strategic partners.

The complaint that AI only gets you to 80% misses the point. Agents can handle the finish work when you give them context. The real bottleneck is product sense.

The boundary between product, engineering, and design is dissolving. AI coding agents and modern tooling enable product developers to own initiatives end-to-end, from insight to production. This is the new default builder.

Quarterly planning worked when shipping a meaningful change took months. That world is gone. AI coding agents ship features while you're still grooming the backlog. It's time to replace quarterly batches with continuous decisions.

AI agents fail at the seams. Rebuild the communication chain with explicit decisions, tight briefs, and fast feedback.

Product work used to ride on tiny, informal moments. Remote work erased that layer. Now AI coding agents are shipping features before PMs can add context—and the rework is piling up.

Larger context windows just create more noise. AI agents need directed attention, not infinite context, to build coherent products.

Coding agents crush refactors but flail on new features when they’re kept in the dark about users, priorities, and constraints.

Hands-on keyboards stopped being the bottleneck. Product judgment, taste, and decision-speed are the new constraints, and AI can help close the gap.

Product managers are drowning in metrics and workflows, but what they really need is clarity, better tools for expressing ideas, and alignment with engineering and company decision-making.

Why missing business context wastes time and ships elegant solutions to the wrong problems.

How to pair AI code generation with business context so teams ship fast and stay aligned with product strategy.

Why AI coding assistants keep suggesting the same wrong solutions, and how context memory solves the endless repeat cycle.