Principle · MoneyThe system prompt alone is only part of the agent definition; the agent mandate defines what the agent should do and what is out of scope and should not be done by the agent. Hallucination control in the mandate includes defining the business impact and why the agent is being built — not just experimenting with technology. Set an ROI expectation for the agent, such as expecting the agent to achieve 70% contract renewal, and measure it at the end to judge business success. Define the policies and boundaries based on what facts the agent should reason over, including isolated context, documents, and connected systems. Security in the mandate defines what systems the agent is allowed to engage with — the speaker wants to sleep at night knowing the agent only accesses three endpoints, not an
AI Engineer · https://www.youtube.com/watch?v=PwCSYy-oGsM
Report · MoneyThe Gemini Live API accepts real-time text, audio, and video frames over a WebSocket connection and responds with real-time audio chunks that developers can stream directly into their application.
AI Engineer · Can Your Agent Hear You Now? Building Live Voice Agents with Gemini — Thor Schaeff
Report · MoneyThor Schaeff explains that the Gemini Interactions API is a stateful API: by passing a previous interaction ID, subsequent generations retain prior context, which in the demo kept visual style and character consistency across image generations.
AI Engineer · Can Your Agent Hear You Now? Building Live Voice Agents with Gemini — Thor Schaeff
Principle · MoneyYoni Michael recommends verifying join safety against actual data by profiling tables to check if join keys are really unique. When attributing ARR by marketing channel, for example, the system checks whether each join key is actually unique rather than picking a path based on column names. Join recommendations should come with actual reasoning and evidence grounded in the data itself.
AI Engineer · The Data Context Layer for Agents — Yoni Michael & Brandon Callender, Typedef
Principle · MoneyYoni Michael advises meeting the model halfway by adjusting tool interfaces to match how the model will query them. Concretely: rename graph query tools to 'search' so the agent uses them without special prompting, and return graph tool results in the same response format as ripgrep so the model knows how to handle them. If you name a tool 'search', the agent will do multi-keyword search into it with no training.
AI Engineer · The Data Context Layer for Agents — Yoni Michael & Brandon Callender, Typedef
Principle · MoneyWhen building auditable semantic analysis on top of deterministic analysis, the important part is to ask many small questions and not one big question. Parse the structure deterministically and then ask many small focused questions that play to the model's strengths. If you write one really good prompt and throw a gigantic model with ten fields at it asking for all the information, it's not going to work — the model will hallucinate, invent information, and not collect as much as it can.
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AI Engineer · The Data Context Layer for Agents — Yoni Michael & Brandon Callender, Typedef
Principle · MoneyThe fix is never just to make the number higher or nicer — it's to build a harder eval: read the traces, find the artifact, harden the harness, and then rerun. Never trust your own scoreboard.
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AI Engineer · The Data Context Layer for Agents — Yoni Michael & Brandon Callender, Typedef
Principle · MoneyA benchmark has to be transparent so you can look at the traces and see what happened; then surprising results make sense. If a benchmark result is surprising, take a look at the traces to find out what is happening. A bar is a claim, a trace is evidence. Click any bar on WolfBench to get taken to the Weights & Biases Weave page where you can inspect all the traces.
AI Engineer · From Zero to Leaderboard: Agent Evaluation — Wolfram Ravenwolf, Weights & Biases
Principle · MoneyWhen showing early prototypes, Josh Woodward says you're looking at people's eyes — that is the metric, not DAU or D7. If someone's eyes light up and they lean in, those are the signals to look for in the early days. He describes asking 'Hey, did you see this? What do you think about this?' and watching for the reaction.
Lenny's Podcast · Why every company now needs to think and operate like a lab team | Josh Woodward (VP Google Labs)
Principle · MoneyJosh Woodward explains that larger lab projects risk crowding out zero-to-one work, so the team uses explicit stage labels — zero-to-one labs, one-to-ten labs, ten-to-one-hundred labs — to keep the distinction clear. The people suited to each stage differ a little too, so you create commonality while respecting the differences. In zero-to-one you optimize for eyeballs; in ten-to-one-hundred you look at waterfalls, CPAs, and conversion to magic moments.
Lenny's Podcast · Why every company now needs to think and operate like a lab team | Josh Woodward (VP Google Labs)
Report · MoneyThe Copilot app has a command called orchestrate for horizontal scaling across repos. Orchestrate lets you set a series of GitHub issues as goals for the agent and spin up sub-agents to address all of those tasks. It spins up agents in the repository they're supposed to be working in, so the agent has permissions in that repository.
AI Engineer · From Your Laptop to the Pipeline: Scaling Custom Agents with GitHub Copilot
Report · MoneyCreate a repository named .github private to expose agent files at the org level. Agents published there appear in the cloud agent dropdown and can be called on the platform. Publishing an agent in the .github private repo pushes it to everyone's IDE or CLI environment so they can call the same agent. This is how you share an agent internally to everyone inside the org; it is purely internal, and an admin can push it out.
AI Engineer · From Your Laptop to the Pipeline: Scaling Custom Agents with GitHub Copilot
Creator perspective · MoneyCodexExec spins up a non-interactive interface great for scripting and CI-CD tasks like auto-fixing a PR, while the app server shines for comprehensive clients and spinning up many Codex threads at once (eval harness, slash goal) because you get full harness control without hand-rolling parallelization.
AI Engineer · Building on the Codex Harness — Dominik Kundel, OpenAI
Principle · MoneyYou cannot dump a massive amount of retrieved context back to your agent; you need a router and summarizer. The router sends incoming prompts to the right place at the beginning, deciding who should handle it. The summarizer takes data from synced context and live context, summarizes it, and sends it back to the agent that sent the prompt.
AI Engineer · Why Your Company Needs a Context Graph (and How to Build It) — Gil Feig, Merge
Principle · MoneyThe definition of 'which customer was upset last week' can mean something very different to one company versus another. Companies need skills like 'how do we determine if a customer is happy' encoded in their context graph. Document company processes — e.g., what to do when a customer complains and how to route to the right place — as skills in the context graph.
AI Engineer · Why Your Company Needs a Context Graph (and How to Build It) — Gil Feig, Merge
Principle · MoneyUse live MCP lookups for simple, single-action queries (e.g., a Stripe lookup for a specific customer). If you need semantic lookups or analysis beyond a simple lookup across a vast dataset, you have to sync a copy of the data. Prefer live API calls when you know you'll have simple lookups and retrievals because they're easier to build, easier to add on, and cheaper to run.
AI Engineer · Why Your Company Needs a Context Graph (and How to Build It) — Gil Feig, Merge
Principle · MoneyEvery answer has to be traceable — never leave yourself in a position where your AI made a bad decision and you have no idea why. Making business-critical decisions on versioned or outdated data without provenance is dangerous: you need to know where data came from, when it was fetched, under what ID and scopes, who accessed it, what permissions were used, whether it was transformed, what it originally looked like, and what should invalidate it. Letting AI make decisions on data synced a year ago without checking provenance leads to bad business-critical decisions. Build provenance tracking into your platform so you can investigate when something goes wrong — dropping provenance tracking leaves you unable to investigate.
AI Engineer · Why Your Company Needs a Context Graph (and How to Build It) — Gil Feig, Merge
Principle · MoneyViren Baraiya's take-away slide: the responsibility of a non-deterministic agent is to plan what should happen next — not to actually do things; the harness is the one that does the actual execution. The harness is a deterministic piece of code that actually understands what is involved in executing a piece of work. When the agent says a cluster is unhealthy and should be restarted, the harness should decide how to restart the cluster and what is involved in restarting it. This is the clear separation between, as he would like to call it, the brains and the hands: the LLM is the brain, the harness is the hands — the agent writes the plan, the harness executes the plan.
AI Engineer · Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya
Principle · MoneyViren Baraiya's operating rule: if the agent writes the plan, the harness executes the plan. When the LLM outputs steps for what should happen next, the harness executes them rather than the LLM doing it directly. Depending on how far ahead the agent can plan, you can execute multiple steps at a time.
AI Engineer · Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya
Principle · MoneyViren Baraiya argues that when determinism matters, the harness should deliver it rather than the LLM. If the LLM decides how to restart a cluster, you lose determinism and every restart could be different. When the agent plans a cluster restart, the harness executes it deterministically so every cluster restart is identical. Non-determinism slipping into critical operations like payments or cluster restarts costs you predictability and safety.
AI Engineer · Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya