Agentic Workers

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Two accounts can have different email addresses. Karpathy's payment example shows why a working-looking implementation still needs human review of identity and data design. #CodeReview #AIEngineering

Uber's MCP lesson: custom integrations built in silos aren't enough. A dependable production system needs a shared development and deployment lifecycle. #MCP #AIInfrastructure

One practical review loop: give the model its own code back and ask it to check correctness, efficiency and construction. Andrew Ng keeps the caveat: improvement isn't guaranteed. From his 2024 talk. #AIWorkflows #CodeReview

Think in larger units of work: one non-conflicting feature per agent, then review the result. Andrej Karpathy on building a delegation habit. No Priors, March 2026. #AIAgents #SoftwareDevelopment

Not every task needs an agent. Barry Zhang's practical test: if the decision tree is clear, build it explicitly. From his April 2025 talk on effective agents. #AIAgents #Workflows

Build, evaluate, iterate. Accenture argues that AI delivery teams need to turn evidence into stakeholder confidence and report what they learned, not only what they delivered. #AIEvaluation #EnterpriseAI

Being able to automate a task does not settle whether people want it automated. Demis Hassabis uses care and empathy to explain the distinction. His perspective in a June 2025 WIRED interview. #FutureOfWork #AI

What would an agentic-engineering interview actually test? Karpathy proposes a substantial project, real tooling and adversarial attempts to break the result, not just puzzles. #Hiring #AgenticEngineering

Business cases ask useful questions. But can you really know the solution, value and delivery cost before experimenting? Jess Grogan-Avignon on learning the business case through the work. #EnterpriseAI #Experimentation

A first draft isn't the workflow. Andrew Ng explains why outlining, researching and revising beats asking for one perfect answer. From his 2024 Sequoia talk. #AIWorkflows #AgenticAI

Vibe coding raises the floor. Agentic engineering keeps the professional quality bar intact. Karpathy on moving faster without handing away responsibility for the software. #AgenticEngineering #SoftwareQuality

Accenture describes a project that took two weeks to build and another twelve months to reach production. The bottleneck wasn't just writing code; it was getting the organization aligned. #EnterpriseAI #Delivery

A simple multi-agent pattern: an assembly line. Tina Huang walks through scanned documents becoming text, summaries, action items and saved data.

Do not stop when the code appears. Tina Huang's reflection example adds a specific review for correctness, style and efficiency, then a revision. Self-review is a step, not proof that code is correct.

A draft is not a finished workflow. Tina Huang explains the difference between asking for an essay in one go and outlining, researching, drafting and revising.

The concern is not just whether AI is conscious. It is using that debate to argue people should lose control of the future. Final part.

The line is simple: build AI for people, and keep the key decisions with people. Part 2 of 3. Next: where the consciousness debate gets dangerous.

The AI consciousness debate can turn into a debate about who controls the future. Part 1 of 3. Next: the line Sam Altman draws.

Part 3 of 3: Customer work changed the hiring story. Alexandr Wang describes how automotive and autonomous-driving projects brought momentum and helped grow the team. Final part of the series.

Part 3 of 3: Thousands of simulated robots train in parallel. The demo then evaluates the policy before deploying it to the real MicroDuck. Final part of the series.