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Mindra Blog
Insights on AI agent orchestration, multi-agent systems, and building adaptive workflows that scale.
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Durable AI Workflows: Why Long-Running Agent Jobs Need More Than a One-Time Run
Real work waits on approvals and other systems for hours or days. A one-time run cannot survive that. Here is what makes an AI workflow durable, explained in plain language for business teams.
How to Tell If Your AI Agents Are Actually Working (and Getting Better, Not Worse)
AI that worked last month can quietly get worse without throwing a single error. Here is how to check whether your AI is actually doing a good job, in plain language for business teams.
How to Write a Runbook for Your AI Department
A runbook is a written, repeatable procedure for a recurring task. Here is how to write one for an AI department, so a coordinated team of agents runs your workflow the same dependable way every time, with the right approvals and a clear definition of done.
How to Evaluate an AI Agent (Team): An 8-Question Buyer's Checklist
Choosing AI to run real work is not the same as testing one chatbot. Use this vendor-neutral 8-question checklist to tell a single AI helper apart from a coordinated, governed team you can actually trust with the operation.
MCP vs OAuth: What You Actually Need to Know About AI Agent Security
MCP and OAuth sound like rivals, but they solve different problems and work together. Here is what each one is, in plain language, how they connect when an AI agent reaches your tools, and why governance on top is what actually keeps a whole AI department safe.
What Breaks When Your AI Department Has 3,000 Tools
Give AI agents access to thousands of tools and new failure modes appear: tool sprawl, wrong-tool picks, permission creep, no record, runaway costs, and security exposure. Here is what breaks at scale and what fixes each one.
Why DIY Agent Stacks Break in Production (and What an Ops Layer Fixes)
DIY agent stacks demo well and break in production. Here are the five failure modes teams hit, the pattern behind them, and how an ops layer fixes it without a rewrite.
AI Agent Observability: What to Monitor Before Agents Touch Production
AI agent observability is more than logs. Production teams need traces, decisions, tool calls, approvals, cost, and outcomes in one place.
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