AI Agent Deployment: The Biggest Mistakes, Costs, and Workarounds
The recurring, avoidable mistakes teams make deploying an AI agent to production, and what actually fixes each one.
Editorial
Evidence-led insights, analysis, and updates on AI agents, MCP infrastructure, and India-first compliance.
The recurring, avoidable mistakes teams make deploying an AI agent to production, and what actually fixes each one.
As agents are granted more autonomy, the human-in-the-loop design pattern is shifting from a blanket approval gate to selective, risk-based checkpoints.
A structured way to evaluate AI sales tools against your actual pipeline, rather than a feature checklist.
What actually needs to be verified before deploying an AI tutoring or practice agent to students preparing for Indian competitive exams.
The specific things worth checking before running an MCP server against real production systems, beyond "does it start."
Specific evaluation criteria for Hindi and Hinglish voice agents that a general voice-AI benchmark won't surface.
Claude Code and Cursor both shipped meaningful updates in August 2026 — faster startup, new model options, and native MCP support. Here's what changed and what it means for teams evaluating either tool.
A practical framework for Indian businesses evaluating AI agents — what to check on compliance, pricing, localization, and deployment before signing a contract.
Open-weight models like Llama and Qwen can be far cheaper at scale, but only past a real usage threshold — below it, the engineering overhead of self-hosting erases the savings. Here's how to think about the trade-off.
A practical checklist for securing MCP server deployments — least-privilege scoping, secret management, transport security, supply chain review, and logging — before connecting one to production systems.
How the Digital Personal Data Protection Act and its 2025 Rules apply to AI systems specifically, and what's phasing in through 2026 and 2027.
Claude Code and Cursor are polished, opinionated products; Cline and Aider are flexible, bring-your-own-model tools. Here's how the four actually differ on architecture, cost, and the tasks each is built for.
India's voice AI market is being pulled in two directions at once — rapid growth in enterprise demand, and a hard technical requirement for genuine regional-language and telephony-quality support that most global platforms weren't built for.
The Model Context Protocol server ecosystem grew fast through 2025 and into 2026, but server counts vary widely by directory, and growth is decelerating as the low-hanging general-purpose servers get built.
India's three most-referenced Indic-language AI efforts — Sarvam AI, Krutrim, and the government's Bhashini — aren't really competing for the same job. Here's how their positioning actually differs.
LangChain, CrewAI, AutoGen, and LangGraph solve overlapping but genuinely different problems. Picking the wrong one for the shape of your workflow costs more time than the framework itself saves.
Where Cursor's MCP configuration files live, how global and project-level config merge, and the security questions worth asking before adding a server — local or remote.
Displaying a price in rupees isn't the same as billing in rupees. Here's the real distinction between AI tools that quote INR for convenience and tools that actually settle, invoice, and accept payment the way an Indian business needs.