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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
The Missing Product Layer for Multi-Agent Systems
Multi-agent systems fail without permissions, handoffs, visibility, and recovery. How AI PMs and engineers should design a product control plane.
 • Nilesh Barla
Why AI Took Coding Before Everything Else
Why AI automated coding before law, design, or strategy, and what the verifiability thesis reveals about where automation goes next for product leaders.
 • Nilesh Barla
How To Design AI Features For Nondeterminism
Why variance, drift, and reasoning failures are not engineering problems, and how to design around them before you ship.
 • Nilesh Barla
Your AI PRD Is Missing Its Hardest Sections
How to write acceptance criteria, failure modes, and behavioral constraints for an AI feature PRD.
 • Nilesh Barla
Embeddings for AI Agents: What Product Leaders Must Know
Embeddings determine what your agent retrieves, remembers, and routes. Here's what every PM and product leader needs to understand about the embedding…
 • Adaline
From Zero To 100,000: The Questions We Set Out To Answer
One year of Adaline Labs. Over 100,000 subscribers. Here's what we believed, what turned out to be true, and what completely surprised us.
 • Arsh Shah Dilbagi
Sub-Agents For Product Managers: Stop Directing A Tool. Start Running A Team.
The chatbot model has a structural ceiling. Sub-agents are what's above it.
 • 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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