Brandon McCrae, Ph.D.

Implementation playbook: if you want daily AI wins, pick one workflow, set one metric, ship a draft-only version, and improve it weekly with real feedback.

AI term, simply: “RAG” means the model answers using your documents instead of guessing. The win is trust, not vibes.

Benchmark reality check: if a model can’t stay consistent across 20 real examples, it’s not “smart enough” for your workflow yet—no matter the demo.

Myth vs fact: “More context always helps.” Fact: more context often adds noise. The best systems retrieve only what’s needed and keep outputs short.

Weekly editor’s desk: the practical AI trend is “reliability over novelty.” Teams are prioritizing evals, approvals, and monitoring because that’s what ships.

Editor’s desk: the teams winning with AI aren’t chasing the newest model—they’re building repeatable workflows with measurement and guardrails.

RAG done right: if your docs are outdated, retrieval makes answers confidently outdated. Fix freshness first, then retrieval.

Security note: don’t let models “see everything.” Segment data by need-to-know; it improves safety and usually improves output quality too.

Market map snapshot: “agent platforms” are becoming the new ops layer—evaluation, permissions, monitoring, and routing are the real differentiators.

Toolchain teardown: the best AI systems look boring—one source of truth, one workflow owner, and clear handoffs. Complexity is where quality dies.

Agent roadmap that works: draft → suggest → prefill → limited actions → broader actions. Autonomy is a privilege earned by reliability.

Observability note: if you can’t trace what the agent saw and did, you can’t debug it. Logs and traces are not optional—they’re the product.

Prompt pattern: “Draft a short answer, then list what you’re unsure about.” It reduces confident wrongness and makes reviews faster.

Implementation playbook: define “done” before you build. If success is “faster,” pick a number; if success is “better,” define what “better” looks like.

Benchmark reality check: for enterprises, “accuracy” is only half the story. The other half is auditability, permissions, and a reliable rollback path.

Agent fail of the week: agents fail most often when they silently proceed on missing info. The fix is boring but effective: ask, escalate, or stop.

Open-source watch: before adopting a hot repo, check commit cadence, issue response time, and whether it has real-world adopters—not just stars.

Eval harness tip: keep a small “golden set” of 25 real examples and run it before every change. If quality drops, you catch it immediately.

UX pattern for AI: show confidence and sources. Users don’t trust “smart,” they trust “explainable and consistent.”

AI News You Can Use: when regulation headlines hit, the practical question is simple—what data can you store, what data can you send, and what requires consent? Source: primary policy text.

Data hygiene note: if your CRM/KB isn’t trustworthy, your AI won’t be either. Clean inputs are the highest-leverage AI upgrade.

Paper-to-product for agents: the win isn’t a bigger brain—it’s better planning, better tools, and a strict “stop when unsure” rule.

Cost note: the cheapest model isn’t always cheapest. If it needs 3 retries and longer context, your “low cost” turns into slow, expensive output.

Myth vs fact: “Agents replace teams.” Fact: agents replace repetitive decisions; humans still own judgment, escalation, and accountability.

Benchmark reality check: a leaderboard score isn’t a business result. Ask: does it reduce errors, reduce time, or increase throughput on your real examples?

Toolchain teardown: if your system relies on 6 brittle integrations, you don’t need a smarter model—you need fewer moving parts and clearer ownership of the workflow.

If your best leads don’t hear back fast, competitors get them. Our speed-to-lead agents draft replies immediately so you book more calls.

Security note: treat tool access like admin permissions—least privilege by default, and approvals for anything that changes money, identity, or customer-facing outputs.

Case-by-case refunds create stress and mistakes. Our agents draft policy-based responses and escalate edge cases so you reduce disputes.

RAG done right: retrieval is not “more data.” It’s “better sources.” One clean source of truth beats ten messy docs every time.