Recording + notebook stay up after. Part 1 of 3 → full course Nov 18.

Tomorrow, 12pm ET: Jev in One Call. 30 min, free, live on Zoom. Meet Choice, Score, Noul on a real support ticket — a dozen questions in one pass. Register: maven.com/p/1d62f8/jev-in-on…

If you need a framework to make your AI agent "safe," your agent isn't safe. Safety is engineered into the system. Not imported from a package. Curious what others think — am I wrong? (Building this live on Oct 31 → luma.com/qwhit9op)

30 min, live on Zoom, recording + notebook yours after. Part 1 of 3 feeding the full bakeoff course, Nov 18.

Free lesson — Wed Oct 21, 12pm ET. Meet the 3 primitives of Jev on a real ticket. One ~100ms call, $0.042/1M tokens, output free. Register: maven.com/p/1d62f8/jev-in-on…

4/ Problem 3: There's no retry logic, no cost ceiling, no audit trail. One malformed query. One infinite loop. One $400 API bill later — you wish you'd built run supervision from the start. I'm teaching this on Oct 31. 4 hours, hands-on, Python only. → luma.com/qwhit9op

3/ Problem 2: The context window fills up and the agent starts forgetting — silently. Token budgets, memory tiers, and conversation compaction aren't optional. They're how you keep the agent from going off the rails 45 minutes into a session.

2/ Problem 1: The agent can call any tool, touch any table, run any query. No permissions. No validation. No policy enforcement. You need permissioned MCP tools — discovery, validation, and policy at the call layer, not the prompt layer.

1/ Most AI agents fail in production not because the model is bad — but because nobody engineered the system around it. Here's what actually breaks (and how to fix it): 🧵

I'm running a live 4-hour workshop on Oct 31: Build an AI Data Analyst you can actually trust — MCP tools, guardrails, context management, fault tolerance. Laptop-only, framework-free Python. 🎃 Details + tickets: luma.com/qwhit9op

4/ The rest of the harness does the same job for the other failure modes: permissioned tools, context budgets, injection defense, retries, traces. It's a 4-hour hands-on build, framework-free, on a laptop. Sat Oct 31 - 19 days out: Register: luma.com/qwhit9op

3/ Then it verifies: - value matches the query output - unit is right (not $12M reported as 12) - reporting period is complete LLM judging is reserved for clarity - not for 'is this number real.'

2/ A report is a set of claims. Every number in it is a claim. A claim is only as good as the query that produced it. So the harness does not trust the model's text. It demands a query for each number.

1/ Deeper on the one failure that actually ships: fabricated numbers. How an agent reports a 12% revenue drop that never exists, and the exact guardrail that stops it -

19 days. Sat Oct 31 - 4 hours, live - we build an AI data analyst you can actually trust, in plain Python. No GPU, no cloud, no API key. $149.99, seats are limited. Register: luma.com/qwhit9op

Last call — free LLM eval gate session is TOMORROW (Oct 7, 2 PM EDT). Regression thresholds + GitHub Actions wiring + merge blocking, all in 30 min. Register: maven.com/p/07c4b3/stop-bad-…

Hot take: most of your AI agent's reliability problem isn't the model. It's that you're letting the model pick its own tools and trust its own numbers. The model should SUGGEST. A harness should DECIDE. Am I wrong?

What does your team’s current LLM quality process look like? Be honest 👇 (Replying with a link to a free session on fixing this — drop your answer first)

Hot take: if your LLM app doesn’t have a CI eval gate, you don’t actually know if it works. You know if it worked last time someone manually checked. That’s not the same thing.