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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.
Jun 13
•
Nilesh Barla
104
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.
Jun 6
•
Nilesh Barla
210
1
May 2026
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.
May 30
•
Nilesh Barla
66
1
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…
May 23
•
Nilesh Barla
156
2
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.
May 16
•
Nilesh Barla
234
1
1
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.
May 9
•
Nilesh Barla
240
2
2
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…
May 2
•
Nilesh Barla
233
3
4
April 2026
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…
Apr 25
•
Nilesh Barla
278
1
3
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.
Apr 18
•
Nilesh Barla
147
1
4
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.
Apr 11
•
Nilesh Barla
318
3
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.
Apr 4
•
Nilesh Barla
212
1
March 2026
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.
Mar 28
•
Nilesh Barla
173
1
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