Today's signal

Today's useful thread is safer ways to use agents at work and more useful business automation. These updates point to agents becoming easier to trust, connect, and put into everyday work instead of staying as demos.

The useful updates

Claude Code gets more room to run longer agent sessions

What changed: Anthropic doubled Claude Code’s five-hour usage limits for Pro, Max, Team, and seat-based Enterprise plans, removed peak-hour reductions for Pro and Max, and raised Claude API limits for Opus models after adding SpaceX compute capacity, according to Ars Technica’s report on the announcement.

Why it matters: If you build with coding agents, the practical ceiling just moved up: longer debugging runs, larger refactors, and more parallel experimentation should hit fewer artificial stops. For small teams, that can mean fewer handoffs back to a human just because the agent ran out of quota mid-task.

Try/watch: Revisit any Claude Code workflows you kept short because of limits, but still track weekly usage and cost; more capacity can also make runaway agent loops more expensive.

Cursor adds context usage breakdowns for coding agents

What changed: Cursor 3.3 added a context usage breakdown so users can see how much of an agent’s working memory is being consumed by rules, skills, MCP connections, and subagents.

Why it matters: This is a practical debugging feature for agent builders. When a coding agent behaves poorly, the cause is often not “bad AI” but too much irrelevant context, conflicting rules, or overloaded integrations.

Try/watch: Open a few real agent sessions and look for bloated rules or integrations that are eating context without improving results. Tightening those inputs may be cheaper than switching models.

Collibra launches oversight for production AI agents

What changed: Collibra launched AI Command Center to monitor and control AI systems and agents across their lifecycle, including ownership, behavior, decisions, and risk signals. The company also announced a Giskard partnership for testing and validation, plus agent assessment templates aligned with AI UC-1 standards.

Why it matters: As agents move from drafting answers to taking actions, leaders need a way to know what is deployed, who owns it, what data it uses, and when it drifts. This is especially relevant for regulated companies and for any business letting agents touch customer, financial, or operational systems.

Try/watch: Before scaling agents, create a simple inventory: agent name, owner, connected systems, allowed actions, review process, and failure plan. Tools like this are most useful when the operating discipline already exists.