Topic

Agentic AI: what holds up outside a demo

An agent that works in a demo and an agent that survives production are different systems. The gap is almost never the model. It is the loop around it: what the agent is allowed to call, what happens when a tool returns something unexpected, and who is accountable when it acts. These are the teardowns, essays and tools covering that ground.

92 pieces

Teardowns

Harness Engineering: The AI Agent Guardrails Nobody Demos The model is a component you swap. The harness around it, tool schemas, validation, sandboxes, budgets, permissions and audit logs, is where nearly every production AI incident actually starts.
When AI Agents for Non-Technical Teams Reach the Shared Drive Agentic tools have moved out of the private chat window and into the folders a team depends on. What that relocation actually changes, checked against vendor documentation as of August 2026.
Tool calling reliability: DeepSeek, Qwen, Kimi open weights and what to check before you build Four separate properties hide inside the phrase "tool calling works". Here is what each of the three Chinese open-weight families actually documents, where the calls break and how to test it yourself.
pass^k vs pass@k agent reliability: your user only gets one run pass@k counts whether a success exists in k tries. pass^k counts whether every try worked. At a 0.61 success rate those read 0.99 and 0.09. Your user gets one run, so publish the second number.
How to Test LLM Applications Without Calling the Model The model is the only part of an LLM application that cannot be tested deterministically. Everything around it can be, and that surrounding code is where most production incidents actually start.
Agent Step Budget and Stop Conditions: What Actually Holds in Production Unbounded agent loops are financial incidents waiting to be billed. How to set a step ceiling against a task class, detect stagnation and bound cost separately from steps.
Signing Off on AI Agent Security Without Lying to Yourself A language model cannot tell your instructions from the text it retrieves, because both arrive as the same token stream. Every real control follows from accepting that rather than trying to fix it.
Designing APIs for AI Agents When the Caller Cannot Read Your Docs An API read by a model has different requirements from one read by a developer. Names become prompt, errors become instructions and idempotency stops being optional once the retry loop is autonomous.
The agent control loop is where production agents fail Prompt engineering was never the hard part. The loop is: what the agent sees, what it may touch, what stops it and what a step costs. Most agent failures are loop failures, not model failures.
AI Legacy Code Modernization Is Not a Translation Problem Legacy modernization was never mainly a translation problem. The hard parts are comprehension and behaviour preservation, and a model's real value sits almost entirely in the first one.
Context Engineering Is Deciding What Earns a Place in the Window Windows grew to a million tokens and the industry assumed retrieval was solved. Attention still degrades, cost still scales, and everything you paste competes for the model's attention.
AI Agent vs Agentic AI: Four Rungs, and Where Your Vendor Actually Sits An agent is a program: a model called in a loop with tools. Agentic is a property of that loop, meaning how much of the goal, the plan and the action the model chooses. A dial, not a category.
OT Security Breaks the IT Playbook, and AI Agents Make It Worse Availability outranks confidentiality, you cannot patch a machine mid-run, and Modbus has no password because in 1979 the wall was the password. Then we connected all of it to AI.
Claude Code in Production: Where It Earns Its Place and Where It Does Not Where an agentic coding tool earns its place in real engineering work, where it does not and why the reviewer is now the constraint. Claude Code as the worked example, with the tradeoffs named.
Context Engineering vs Prompt Engineering: One Task, Two Fixes, Two Bills Prompt engineering optimises the wording. Context engineering decides what is in the window at all. As windows grew the bottleneck moved from phrasing to selection, and the bill moved with it.

Agentic AI: what holds up outside a demo

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