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Warp’s Ben Holmes scores conversations, tool calls, and code changes to find recurring failures and propose reviewed repairs to agent instructions and setup.

Anthropic’s Lance Martin covers prompt debt, cache reuse, evaluation-driven optimization, and recoverable agents, with a detailed Q&A on overfitting and human judgment.

Neo4j and Blitzy use an interest-calculation example to explain traceable dependency graphs, approved plans, and old-versus-new behavioral tests.

Matt Wolfe edits an animated image, tests hosted video demos, and finds the practical limits behind speed claims, open weights, and free allowances.

Former Cursor designer Ryo Lu uses a journal-app demo to show how editable plans, small changes, and collaboration help people develop their own judgment.

Google DeepMind’s Philipp Schmid combines a sandbox, skills, tools, proxy-managed credentials, hooks, and schedules in a finance-agent demonstration.

IBM’s Mixture of Experts separates targeted model repair from recursive self-improvement, then examines open weights and the limits of world-model simulations.

Sid Pednekar uses two hypothetical asset-management firms to explain problem selection, shared capabilities, and learning after AI deployment.

CentralAgent AI’s Sparsh Jain turns repeated corrections into cases, strategies, and skills maintained through a memory lifecycle and evaluation loops.

Coatue’s Frank Long breaks investment research into testable steps run by code, statistical models, language models, and people.

Keycard and Temporal demonstrate an expense agent that preserves user delegation across services and resumes settlement after a worker crash.

Netflix’s Cameron Wolfe connects calibrated human review, automated judges, and production experiments to make subjective AI evaluations more reliable.

Website makeovers, video ads, tutoring, assistant bots, CAD models, audio briefings, bid alerts, and uptime monitoring, with proposed service packages.

IBM’s Martin Keen separates the need for semantic search from the need for a separate database, using a four-level retrieval pyramid.

Blackstone and Norm AI CTOs discuss usable data, replaceable workflows, durable expert tests, and when to build, prototype, or wait.

Brainbase’s Domino’s pricing case pairs explicit definitions, browser traces, and repeated tests to improve a narrowly scoped agent.

Wise’s Emil Chetty separates customer authorization from agent planning, with locked payment jobs and checks in the services that move money.

Cole Medin tests three coding-factory configurations, finds costly rework in long runs, and keeps open models on scoped implementation.

Alexander Shevchenko explains Ramp Labs’ work on financial retrieval, precomputed context and parallel security agents, with task-specific company results.

Nous Research’s Dillon Rolnick argues that agent platforms create switching costs, and makes the case for open foundations with replaceable models.

General Intuition’s Pim de Witte describes a pixels-to-actions model trained on labeled gameplay, with claims of transfer across robot controllers.

Hamel Husain compares six classifiers on leasing-assistant traces, then shows how to tune thresholds, check confidence scores and weigh cost.

Matt Wolfe builds reusable scripting and editing skills, then fixes a rough cut through feedback, waveform checks and saved workflow changes.

A shared patient brief, organized records, better questions and expert updates: Peter Yang's five-step caregiver workflow, with clinical decisions left to the care team.

Langfuse's banking case combines representative test data, calculator tools, separate quality gates and expert review to decide when an agent can ship.

Datasite's CTO explains how document edge cases changed its build-versus-buy decision, and how a private benchmark checks the downstream answers.

NatWest's routing funnel, human reviews and miscalibrated judges show what it takes to prepare a customer-facing banking agent for release.

Basis co-founder Mitchell Troyanovsky explains how behavior specs, replayed decisions and lightweight simulations help teams trust production agents.

George Cameron breaks down an agent benchmark's $21 task cost, its over-$200 uncached estimate, and how to choose models against your own workload.

Simile's Joon Sung Park explains interview-grounded behavioral simulation, its validation problem, and the gap between customer panels and predicting society.

IBM's Jeff Crume explains data visibility, classification, jurisdiction, access controls, and continuous audit evidence for AI systems.

Neo4j's Zachary Blumenfeld turns skills and decision traces into executable graphs, with explicit routing and inspectable loan-approval runs.

Factory's Nikhil Harithas explains customer-owned agent compute, outbound relays, and the lifecycle machinery behind portable coding agents.

Aamir Shakir explains multimodal retrieval, demonstrates search over Intel reports, and reports a customer accuracy gain with important limits.

Cristóbal Valenzuela demonstrates Continuum, a research prototype for interactive visual explanations and shared visual work with coding agents.

A dashboard for human decisions, agent run histories, workflow changes and regression tests, backed by Archon and Postgres.

Will Brown makes the case for turning agent traces and human decisions into tests and training data for organization-specific models.

Figma's Ezra Mechaber shows a shared product-management skills repo, agent-assisted testing and an experiment workflow.

IBM's Legare Kerrison walks through six inputs that help a coding agent work within an existing repository.

Speakeasy's Sagar Batchu lays out identity, policy and audit controls for agents that can act on production systems.

Sourcegraph's Andrew Norrish connects review pressure with incident response and outlines three code-intelligence workflows.

Adam Nahari argues that AI projects need a measurable business problem, workflow redesign and committed users to produce financial impact.

Zack Proser shows how he turned writing experience into a tool for colleagues, and applies the same idea to bedtime stories for his children.

Nick Nisi turns code changes into simulated smells and proposes a risk-based review pipeline.

Maddy Zhang argues that verification, judgment, agent workflows and communication matter as coding agents change engineering work.

Jaclyn Konzelmann discusses short strategy documents, user co-creation, experiments, and designing for an agent-filled world.

Avery Blanchard explains the three compilation stages, graph breaks, and how to measure whether they help.

Compound's Yijia Liang explains shared agent inboxes, Office-compatible engines, and checking loops for financial deal teams.

Ramakrishnan Lokanathan on testing complete AI workflows, retaining regression checks, and turning real failures into evaluations.

Oracle's Jeff Smith explains vetted reports, schema context and database-level permissions as ways to limit the risks of connecting agents to company data.