Method · MoneyThe complete cycle: the application produces traces, online evals grade them and produce labels and explanations, labels feed monitors, monitors alert the team, the team investigates failing traces and saves them as a regression dataset, the agent is improved, an experiment verifies the fix, and it ships — with returning traffic graded by the same online evals. Every caught failure becomes a new test case and regression test.
Method, not a verified protocol.
AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI
Method · MoneyTo verify an agent trajectory: generate a good rubric with N criteria of what success looks like; rank the trajectory's screenshots by relevance for each criterion and use the top-K evidence to determine whether each criterion was met; check for contradictions between what the agent said it did and the actual environment state; output a scored rubric with process score (partial credit in some cases) plus an outcome Boolean for whether the agent accomplished the task as a reasonable user would expect.
Method, not a verified protocol.
AI Engineer · Your LLM Judge Is a Confident Liar: Building Better Verifiers — Browserbase
Method · MoneyRun the loop: (1) understand the executive outcome (the target), (2) deliver the value proof by running the project with the CSM, (3) communicate the value back to the customer and get acknowledgment, (4) get the next outcome and repeat — as many times and as fast as possible. The more and faster the loop runs, the more opportunities to sell.
Method, not a verified protocol.
30 Minutes to President's Club | No-Nonsense Sales · #604 - Upsell & Expansion Masterclass: 7 Lever to Grow Any Account
Method · MoneyWarrick's core method for human-in-the-loop agent systems: structure the agent loop as a Temporal workflow, use a wait condition to pause the workflow while tracking for a signal, and use the signal to capture the human's response and resume work. She states this is the pattern to use whether the human or the computer initiates the pause, and whether the service is offline or back online.
Method, not a verified protocol.
AI Engineer · The Human Is an Async API — Melanie Warrick, Temporal
Method · MoneyHarald Kirschner describes the pipeline: every time an exception occurs in an app, an error stack is reported. VS Code builds error-stack tooling on its own data instead of using external tools. They collect raw telemetry and filter it down to error stacks that have the full stack (around 51 billion telemetry events per day before filtering), then group and bucket filtered error stacks as a fingerprinting exercise. After filtering and bucketing, 10 issues are filed assigned to specific area owners, and a pull request is auto-created to try to fix the problem. All error stacks are displayed in an error page showing hit counts and number of users affected so the team can quickly react. The agent investigates filed issues to find out why the error happened and who was responsible. In one case
Method, not a verified protocol.
AI Engineer · How VS Code Went from Monthly to Weekly Releases with AI — Harald Kirschner
Method · MoneySet up Sales Navigator alerts to identify when VP+ or executive-level employees join your company, then immediately connect with them to leverage their networks for warm introductions.
medium
Method, not a verified protocol.
30 Minutes to President's Club | No-Nonsense Sales · Introducing: The Warm Pipeline Machine (Social Selling Course)
Method · MoneyMeasure AI adoption across three dimensions: Utilization (DAU/WAU), Impact (quality/speed/value), and Cost (token spend). Use API telemetry to create cohorts and compare against foundational metrics.
Method, not a verified protocol.
AI Engineer · The State of AI in Software Development: Data from 400+ Orgs — Justin Reock, DX
Method · MoneyAngel Steger describes the architecture technique of 'translation': starting with a high-level abstraction (e.g., site map), solving one problem at that level, then switching materials or views to solve the next layer of problems, avoiding the trap of trying to solve everything at once.
Method, not a verified protocol.
Design Better · Angel Steger: Wealthfront's VP of Design on what architecture teaches you about software
Recommendation · MoneyJohn Stecher advises getting your data story right before attempting to succeed with AI agents: move data out of transactional systems into a store that can be easily accessed, managed, and recalled from an agent perspective. He warns that companies that want to build many AI agents but lack a cohesive data strategy will fail.
AI Engineer · Why Waiting for the Next AI Model Can Pay Off - John Stecher, Blackstone & Scott Worland, Norm AI
Recommendation · MoneyJohn Stecher advises thinking through what your eval set is for testing your agents, and running evals consistently to measure the health of your AI system — he calls this paramount to success. In financial services the quality bar for what agents have to deliver is very high, and the duty to be accurate is paramount.
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AI Engineer · Why Waiting for the Next AI Model Can Pay Off - John Stecher, Blackstone & Scott Worland, Norm AI
Recommendation · MoneyScott Worland recommends building a large set of evaluation samples — tens of thousands, hand-labeled by experts — so you can regression-test your AI system. When it became clear agents were viable, Norm AI was able to take this benchmark set and immediately run more agentic approaches on top of it. He also recommends working directly with domain experts (lawyers) to stand up these benchmarks and evaluations instead of a traditional roadmap, and building a scorecard that determines when an agent or human is successful at completing tasks across various dimensions, then watching it improve over time.
AI Engineer · Why Waiting for the Next AI Model Can Pay Off - John Stecher, Blackstone & Scott Worland, Norm AI
Recommendation · MoneyThe speaker strongly advises always testing the whole workflow and not just the final answer. He illustrates with an anecdote: he asked a model to change only the background of an image, and the output changed the background to cream but added two handles — showing that checking only the final output misses workflow errors. He discourages looking only at the start point and end point of a workflow and assuming the objective was achieved, since the background may have changed but the objective may be broken. If you have a hundred steps in your workflow, each step has to be evaluated for what goes into that step, what comes out of it, and what the expected outcome is.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyJeff Smith describes piping agent access through organizational single sign-on (like Azure Entra ID), which returns a list of scopes and roles describing the user's role in the organization. The database can then be coded to enforce role-based access, e.g., an HR manager can see everyone's salary but no one outside that org can. When natural language questions are generated to SQL, the database dynamically strips data from the result set before it comes back to the user, enforcing data security.
AI Engineer · Your AI Agent Wants Your Data. Here's How to Lock It Down - Jeff Smith, Oracle
Recommendation · MoneyWhen reaching out to an executive after meeting their team, open with a script that names the team members, summarizes their main hurdles, quantifies the impact, and asks to validate the perspective together.
30 Minutes to President's Club | No-Nonsense Sales · #625: 18 Deal Cheat Codes to Cut Your Sales Cycle in Half
Recommendation · MoneySet a redline deadline — a set of deadlines for when the customer's legal team delivers first-round redlines, when you respond, and when they return their second response — to project-manage the vendor review stage. Be maniacal about project-managing the customer through the legal process once you send over a contract, because that stage is where sellers most often fail. Even without a commercial give-get to back it, set the deadline anyway: just stating it incentivizes the customer to hit the dates and gives your champion leverage to push their legal team.
30 Minutes to President's Club | No-Nonsense Sales · #625: 18 Deal Cheat Codes to Cut Your Sales Cycle in Half
Recommendation · MoneyGet the next meeting in the calendar before ending the conversation — if you are losing deals, this is probably why. Depending on your ICP, prospects will really respect the practice.
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30 Minutes to President's Club | No-Nonsense Sales · #625: 18 Deal Cheat Codes to Cut Your Sales Cycle in Half
Recommendation · MoneyEarly in the sales process, meet with end users to learn what is working and what is not, then package those findings into a point of view that can be used as leverage to secure a call with the executive team.
30 Minutes to President's Club | No-Nonsense Sales · #625: 18 Deal Cheat Codes to Cut Your Sales Cycle in Half
Recommendation · MoneyThe moment you are picked as vendor of choice, ask your champion to kick off procurement, security, and legal at the same time instead of sequentially — this alone can turn a six-week review into two weeks.
30 Minutes to President's Club | No-Nonsense Sales · #625: 18 Deal Cheat Codes to Cut Your Sales Cycle in Half
Method · MoneyDaksh Gupta lays out a basic recipe for building a review agent: get yourself a sandbox (you can use Firecracker instead of buying one from a vendor), put an agent in the sandbox (the agent can be Codex or Claude Code; he personally recommends going model independent, using something like OpenCode or Pi as an open-source harness), connect your GitHub or GitLab with a PR webhook that sends the contents of a pull request when it is opened, give the agent a simple prompt like 'review this and send back some feedback', and wire the agent's feedback back using GitHub's review APIs. About five minutes later, you will get some feedback on your pull request.
Method, not a verified protocol.
AI Engineer · How to Build an AI Code Review Agent - Daksh Gupta, Greptile
Method · MoneyKevin Hou describes an agent-automated eval workflow: load a skills file and ask questions in natural language; take a set of tasks and run multiple rollouts; compare control vs experiment tables and ask the agent to compute the delta; have the agent propose hypotheses for why deltas occurred; spin up subagents to investigate each hypothesis in parallel and map results back to a single response; review the report and interact with the data via generative UI (dropdowns, filtering, segmenting, slicing); then iterate for a better next experiment. He notes this replaced slow manual Jupyter Notebook analysis and hand-engineered judge pools and data pipelines, turning a very manual eval process into one that takes minutes.
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Method, not a verified protocol.
AI Engineer · Get Out of the Model's Way — Kevin Hou, Google Antigravity
