Ai-Engineer

Kamil Potrec explains agent identities, delegated permissions and credential gateways, including the network controls and operational costs they require.

Patrick Smyth separates malicious packages, known vulnerabilities, zero days and compromised build tools, then maps a different defense to each.

Fuad Ali explains Alex's production design: harness-owned plans, explicit completion, file-backed results, small auditable memory and regression tests drawn from real sessions.

Mikyo King explains Phoenix's code-mode design: discover tools, compose them in code, and return smaller results, while handling secrets, permissions and failed executions.

John Feminella connects Citibank’s mistaken Revlon payment to five agent controls: ownership records, risk-based review, scoped tools, shared tests and selective approvals.

Tim Tyler's Agent Accountability framework separates agent identity, human ownership, task scope, permissions, outcome checks and intervention.

Runlayer's Aiden Sochowski connects agent session logs to narrower permissions, prompt-injection defenses, broken-tool detection and spending controls.

Optiver's Noyan Tokgozoglu explains internal model tests, portable agent tooling, context-cost controls and the limits of replaying live workflows.

Read traces, group recurring failures, save regression cases and compare a proposed fix against them. Fuad Ali demonstrates the loop with Arize Signal and keeps humans responsible for judging issues, tests and releases.

Put realistic data in fresh sandboxes, check what the agent changes and compare runs task by task. Xander Song shows how Phoenix tests its agents with Harbor, including a SQL-tool optimization.

Target real incident patterns, test against historical pull requests and supply codebase context. Greptile's Daksh Gupta explains model diversity, executable sandboxes and the cost of noisy automated reviews.

Separate conversation history from a shared cloud workspace, then hand files between specialized agents. Jack Wotherspoon demonstrates Gemini Managed Agents, credential proxies and scheduled workflows.

Measure the silence callers hear, keep listening while tools run and test the complete conversation. Jesse Hall shows how LiveKit evaluates voice agents against interruptions and real back-end outcomes.

Start with real traces, define one concrete pass/fail criterion, build a model-based judge and compare its decisions with human labels. SallyAnn DeLucia walks through the process in Arize.

Arize's Aparna Dhinakaran explains the work behind always-on agents: trace-driven triggers, disposable sandboxes, durable state and evaluators checked against human judgment.

Bridgewater and turbopuffer explain why document permissions must shape the search itself, balancing relevant results with fast access checks.

Browserbase separates reliable cloud browsers from adaptive page control, with receipt collection, vendor payments and business-verification examples.

Kepler separates language-model planning from financial facts and arithmetic, then checks every displayed number against a citation registry.

Kaggle's benchmark audits expose fragile scores. LinqAlpha's MandateBench tests the reasoning behind a full investment memo, with early results from ten tasks.

Laurie Voss argues that faster coding moves the bottleneck toward defining what users need. His product-engineer forecast also demands a new apprenticeship for junior developers.

Sofia Jakovcevic builds a regression-testing loop from production traces. Keep separate scores for safety and task completion, and protect workflows that already work.

Bridgewater's Vault combines governed documents, measured search quality and familiar research workflows. Sam Green and Aaron Linsky explain what people and agents need from it.

Harneet Kaur tests two models on iHeartMedia. Checking each reasoning layer makes their forecasts converge, but agreement leaves accuracy unproven.

Contour's Hari Kumar compares research workflows with and without AI, tracks cost per useful insight and counts the time lost fixing broken tools.

Aman Singh explains how Arca coordinates days-long client work with event signals, persistent memory, shared plans and human approvals.

Andrey Rybka maps agent incidents to scoped identities, checked tool calls, tenant isolation and outbound controls that include DNS.

Jump Trading’s compute leads explain bespoke GPU contracts, startup prepayments and why a price hedge cannot deliver a cluster.

Joseph Kim describes licensed financial data, firm templates and inspectable citations, then separates today’s product from his 2027 predictions.

Carmen Li explains reserved GPU procurement, provider verification and forward pricing, separating delivered capacity from financial hedges.

Freddy Lecue describes Wells Fargo’s transaction-trained transformer, its data-tokenization work and reported fraud gains over the bank’s XGBoost baseline.

Isabelle Williams demonstrates agent registration with auth.md, user-approved identity sharing, and separate revocable agent tokens with limited permissions.

Stephanie Jarmak explains how realistic workloads, repeated runs, trusted traces and enforced quality gates make agent tests useful for real decisions.

San Francisco Compute’s Evan Conrad argues that deposits, delivery delays and uncertain demand form one financing problem. Tradable capacity needs reliable cluster operations behind it.

Jellyfish’s Nicholas Arcolano finds that skills, usage, team environment and individual differences explain more variation in merged code changes than tool choice. Quality remains outside this analysis.

A review of 1,368 reported AI incidents motivates tests of permissions, human review and monitoring alongside model accuracy. The findings are retrospective, not proof of prevention.

Air Canada’s Nathanael Weill starts with known answers, generates test inputs around them and checks results in code. Synthetic tests speed iteration but do not authorize release.

Tone-Bench tests whether models can write in one person’s voice. Louis-François Bouchard explains its rubrics, judge bias and the cost of changing a test.

A six-stage creator workflow built around reference libraries, reusable skills, editing tools and human approval—not a promise of automatic audience growth.

Match engineering rigor to failure consequences, dependencies and replacement cost—and make the rigorous path cheaper for humans and coding agents.

GEICO's Ford Prior brings decision makers and agent fleets into one workflow, pairing legacy-code research with human approvals, reusable diagrams and explicit cost controls.

Vanguard's cash-ledger migration pairs AI-queryable legacy code with daily data checks, migration documentation and visibility into producers and consumers.

MiniMax's Morgan Suo connects faster model internals to the cost, accuracy and delivery time of a research report an analyst can actually use.

Oliver Kennon explains how serving software, reliability and security change the cost of useful AI output, even on the same GPU.

Monzo's Agent Chip grew from a Slack reaction into a reusable internal service. Its speakers explain the platform, permissions and sandboxes behind that growth.

Al Chen of Cisco explains how exploit tests, verified restores and privacy checks help banks move AI into production. The bottleneck also includes image approvals, GPU procurement and internal ownership.

Moshi founder Anekha Sokhal argues that AI should gather and check market information while investors keep the decision. She describes a six-agent system, traceable claims and the hidden cost of losing apprenticeship.

Diogo Almeida argues that pleasing a human and running reliable software are different objectives. His case for moving beyond AI assistance—and why he says Jev's approach is not simply RLVR.