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What Is an Agentic Stack, and Why Does It Matter More Than the Model?
An agentic stack routes work, controls context, permissions, verification, and approval, and matters more than the model powering it.
 • Nilesh Barla
What Is Loop Engineering, and Who Owns It?
The loop engineer owns an AI agent's runtime. Three primitives, five maturity levels, and where the role emerges inside production teams.
 • Nilesh Barla
Agent Replay Is A Product Surface, Not A Debugging Feature
Agent replay for production AI agents: what to capture in every trace, who it serves, and why to design it in from day one.
 • Nilesh Barla
The Long-Horizon AI Agents Ceiling Is A Product Problem
The planning ceiling for long-horizon AI agents is real and moving slowly. Five product moves now bypass it, including embeddings-as-memory for…
 • Nilesh Barla
The Self-Improving Agent Is A Production Pattern Now
The self-improving AI agent is a real production pattern now. What agentic harness engineering is, and the five layers that build one.
 • Nilesh Barla
Chat Is the Wrong Default for AI Products
Why the chatbox became the default AI interface, the four patterns replacing it in 2026, and a three-question diagnostic for your product.
 • Nilesh Barla
Prompt Injection Is Not a Prompt Problem
Prompt injection is not fixed by better prompts. The attack surface lives in the tool layer. Here is what actually closes it.
 • Nilesh Barla
The Operating Loop: How Production AI Agents Actually Get Better, And Where The Loop Breaks
Most production AI agents are not self-improving; they are running on static prompts and informal patches. The operating loop is what changes that.
 • Nilesh Barla
What Happens When Your AI Agent Interacts With Everything
MCP connected your agent to everything. Performance drops up to 85% as tool count grows. Here's a practical framework for choosing the right model…
 • Nilesh Barla
The Tool Selection Problem: Why AI Agents Call The Wrong Tool And How To Fix It
AI agent tool calling fails for predictable reasons. Four failure modes trace back to description quality, not the model. Here's the fix.
 • Nilesh Barla
Building AI Agents That Don't Break in Production
Your agent works in the demo. Production AI agents face five failure modes simultaneously. This guide maps all five and links to what fixes each one.
 • Nilesh Barla
Agent Memory Is A Product Surface, Not Saved Chat History
Learn how to design AI agent memory as part of context engineering, including what agents should remember, forget, retrieve, evaluate, and log in…
 • Nilesh Barla
Reliable Tool-Using AI Agents In Production: MCP, State, Retries, Timeouts, and Recovery
Learn how to build reliable tool-using AI agents in production with MCP, stateful tools, retries, timeouts, recovery patterns, approvals, and…
 • Nilesh Barla
How To Evaluate Coding Agents In Production: Metrics, Failure Modes, And Review Loops
How to evaluate coding agents in production: four metrics that matter, five failure modes to design against, and a review loop that compounds.
 • Nilesh Barla
Adaline Labs
Adaline Labs
The newsletter that swaps stale buzzwords for actionable insights. Our research-backed articles, expert commentary, and bold experiments with LLMs serve one purpose: to spark inventive thinking. By Adaline(.ai).

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