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What Product Leaders Should Stop Doing Now That AI Can Do It
A practical framework for removing low-leverage work without outsourcing judgment.
Jul 25
•
Nilesh Barla
174
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.
Jul 18
•
Nilesh Barla
103
2
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.
Jul 11
•
Nilesh Barla
127
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.
Jul 4
•
Nilesh Barla
167
1
June 2026
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…
Jun 27
•
Nilesh Barla
110
1
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.
Jun 20
•
Nilesh Barla
83
4
3
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
108
1
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
212
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
2
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
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