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Implementation notes for building AI tools around real business data, handoffs, review queues, and safeguards.
Resource path
This lane is for teams turning AI from a demo into a system that touches real business data. The throughline is implementation discipline: handoffs, receipts, evals, observability, review lanes, and the boundary between proposed work and approved action.
Start here
Agent receipts: what to log before AI touches customer workIt shows what a reviewer needs before approving AI-assisted work: source, rule, approval, owner, and rollback context.
Define the evidence, approval, receipt, and rollback path around one AI system boundary.
Map one review laneA coding agent, support agent, or data assistant needs a lane before it needs more autonomy: owner, evidence, allowed changes, receipt, and rollback.
Capture what the system read, what it proposed, which rule it used, who approved it, and how the team can reconstruct the decision later.
Before launch, check whether evals and dashboards measure the handoff: missing evidence, risky action, reviewer correction, and quality-lane drift.
Constructed diagramGitHub's repository-level Copilot metrics now split human pull-request review time into three stages. The distribution can help teams find a queue without mistaking it for an AI productivity score.
Constructed diagramGitHub Copilot CLI now reuses a persistent C++ symbol index. The first scan costs time and memory, but subsequent navigation can use information from files that are not open.
Constructed diagramThe Copilot app can now restrict local agent commands by project. Its sandbox starts off, and its default policy still permits network and credential access.
Constructed diagramOpenAI added a caching dashboard, miss diagnostics, and explicit breakpoints for GPT-6. Here is how to test whether a long-running agent actually reuses its input.

GitHub’s review update separates newly introduced issues from newly discovered ones. Teams that read only inline comments can miss part of the review.

V7’s document graph shows why useful AI context depends on relationships between records, not just the number of files an agent can search.

Astra for Law combines GPT-6 Astra with legal search and instructions. Its benchmark results and source-coverage figures answer different buying questions.

A Copilot budget approval sets a new member total. Route the request to the paying account and check the other limits before assuming work can resume.

Better transcription is useful, but a workflow may depend on one email address, code, or amount. Evaluate those details separately from overall word error rate.

Cooley describes an AI-assisted IPO workflow built around client information, public sources, and curated precedents. The useful lesson is how teams choose a draft’s starting point.

OpenAI now operates the Codex harness through an API. Developers can choose the execution environment, but self-hosted compute does not remove managed-session data limits.

Salesforce brings CRM context and actions into Claude. Start with a supported sales task, check the answer against records, then evaluate updates separately.

Product notes, service updates, and BaristaLabs news that affect how small teams use AI at work.

AI market news translated into workflow decisions, risk boundaries, and practical next steps for small businesses.

Model concepts explained through thresholds, queues, and error costs that small teams can actually manage.

Plain-language guidance for owners and operators choosing one useful, reviewable AI workflow at a time.

Hands-on guides for approval policies, shadow weeks, agent receipts, and other AI workflow controls.