Ai-Engineer

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

Oracle's Richmond Alake proposes an intelligence plane for agent knowledge, memory, data access and learning.

Bumble's Supriya Subudhi shares five lessons from testing AI photo feedback: adoption, bad outputs, costs, model choice and regional rollout.

imec benchmarks coding agents across models, harnesses and hardware, finding that utilization and concurrency can change the economics of self-hosting.

Upstart and Coval describe human review, AI judges and voice simulations for a regulated loan-verification agent.

Oracle's Wojciech Pluta demonstrates a memory-promotion gate that retains an operator's preferences while rejecting an unsafe blanket lending rule.

Killian Carlsen-Phelan shows Sonar Vortex supplying project context before an agent writes code, then rejecting a temp-file permission fix that still fails the rule.

Jack Woodruff argues that faster research raises the baseline for investment work, while auditability and deciding what deserves attention remain human bottlenecks.

Rayan Krishnan shows how task-specific tests can reverse model rankings, expose hidden token costs, and turn a firm's past work into a buying guide.

Sonar’s Gitar workshop checks three ticketing-app changes: a refund bug outside the diff, an all-in pricing rule, and a failing check-in test.

Sonar CEO Tariq Shaukat argues for explicit intent, guidance, independent verification, and evidence as coding agents outpace human review.

David Abitbol reports Revolut’s experience serving 2,000 engineers with GLM models: lower token bills, faster delivery, and substantial GPU-serving work.

Temporal’s Shy Ruparel separates agent adoption from production reliability, then shows how recorded workflow history can preserve work across worker crashes.

Qodo’s Cole Gannaway explains cross-repository review, independent review agents, and turning recurring review comments into shared rules.

Addy Osmani and Manish Kapur discuss first-pass agent review, browser evidence, layered checks, and the human ownership behind larger code-change volumes.

Sonar's Hunter Agent maps a repository and challenges logic-bug findings. A prepared Python demo shows 46 issues, without a detection-accuracy benchmark.

Chris Vela demonstrates BenchPod, a vLLM benchmarking dashboard, and compares serving configurations against concurrency and response-speed needs.