Towards Spec-Driven Test Automation, Part 4

The post Skill or MCP Server? Ask What Stops the LLM From Disobeying appeared first on Towards Data Science.

Turn scattered requirements into deployable agents

The post What's in the Box? Distinguishing Acceptable Traces from Unacceptable Ones appeared first on Towards Data Science.

A million-token output window changes what you can hand a model in one go, and the benchmarks and the bill show who it actually suits.

The post Welcome to Google Gemini 4 Argon appeared first on Towards Data Science.

A hands-on Jev repo comparison of routing accuracy, latency, and output reliability

The post Jev Your Engines: Seven Open Decision Models vs. a Generative LLM appeared first on Towards Data Science.

Pasting in the whole chat history cost 20 to 31 times more than looking up notes, and it did no better on the tasks.

The post I Tested Four Ways to Give an AI Assistant a Memory. The Obvious One Was the Worst Value. appeared first on Towards Data Science.

A technical reflection on why data scientists need software engineering principles when AI agents write code.

The post A Scaffold at the Gate: Building AI Applications That Can Evolve appeared first on Towards Data Science.

Agents got good. The hard part now is everything around them.

The post Your Agent Doesn't Need More Freedom appeared first on Towards Data Science.

Real repositories, seeded agent decisions: what 50,400 runs show about wasted turns, patch churn, and which harness rules help

The post Coding Agents Can Solve the Task — But How Much Work Did They Waste? appeared first on Towards Data Science.

Re-reading my own IBM patent on real-time concert feedback as what it actually was: an agentic loop with multimodal perception, memory-grounded inference, and a human in the loop. What it got right, and what a decade of hindsight teaches agent builders today.

The post We Specified an AI Agent in 2016. We Called It Analytics. appeared first on Towards Data Science.

Add architectural guardrails around how agents read untrusted sources and use them in the workflow.

The post How Can AI Agents Read Untrusted Sources Safely? appeared first on Towards Data Science.

AI Agents for Beginners, Part 1

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AI agents can now write more code than any of us can read. That changed how I think about AI.

The post Stop Using AI. Start Hiring It. appeared first on Towards Data Science.

Mapping structural variance against execution outputs to estimate coding agent reliability without ground truth.

The post The Consistency Quadrant: A Visual Guide to LLM Reliability appeared first on Towards Data Science.

Towards Spec-Driven Test Automation: Part 3

The post A Google Team Measured Half of My Argument, and Left the Other Half Open appeared first on Towards Data Science.

A controlled test of Gemini, DeepSeek, ChatGPT, and Claude on leakage, reporting delays, promotion effects, and structural breaks.

The post I Hid Four Traps in a Forecasting Task. Here Is What Four AI Assistants Did. appeared first on Towards Data Science.

From guarding one agent to steering a fleet

The post How to Govern AI Agents appeared first on Towards Data Science.

Trying to answer the question of "Can LLM agents discover?" through the lens of creativity

The post Measuring the Creativity Potential of LLM Agents appeared first on Towards Data Science.

Coordinating agent capability development with the application it powers

The post Where the Agent Development Lifecycle Fits appeared first on Towards Data Science.

Giving an LLM permission to act, in 9 steps

The post How to Build a Control Plane for AI Agents appeared first on Towards Data Science.

I traced 2,500 listing checks with Weights & Biases Weave, removed avoidable model work one change at a time, and scored every version against the same answers.

The post Can an Apartment Search Agent Call the Model Fewer Times and Still Find Good Matches? appeared first on Towards Data Science.