MCP at 97 Million Installs: The Protocol That Became AI's TCP/IP
Anthropic's Model Context Protocol hit 97M installs and joined the Linux Foundation. As a Technical Lead, here's what this means for how we build AI systems today and tomorrow.
#agents
11 posts
GPT-5.4 and the Autonomous Agent Era: What Technical Leads Need to Know
OpenAI's GPT-5.4 crosses the human baseline on OSWorld-V with native computer-use, 1M token context, and parallel tool calling. Here's what it actually means for teams building AI systems in 2026.
Agentic AI in Production: What Stripe, OpenAI, and AWS Are Building Right Now
Stripe's autonomous agents write 1,300 PRs per week. OpenAI extended its Responses API with shell tools. AWS launched Strands Labs. The agentic shift is no longer theoretical — here's what it looks like in the real world.
MCP at 10,000 Servers: How a Protocol Became the Agent Integration Standard
The Model Context Protocol crossed 10,000 published servers under the Linux Foundation's Agentic AI Foundation. As someone who's integrated dozens of AI systems, here's why this number matters more than any benchmark.
AI Agents in Production 2026: The Shift from Copilot to Autopilot
90% of developers now use AI at work. But the real shift in March 2026 is agents moving from suggestion-mode to autonomous execution. Here's what that actually looks like in production systems and what breaks when you go too far too fast.
Claude Opus 4.6 Agent Teams: Multi-Agent Development Is Here
Anthropic's Agent Teams feature in Claude Opus 4.6 lets multiple Claude Code instances work in parallel on the same codebase. Here's the architectural model, real-world performance data, and what actually changes for teams building production software.
GPT-5.4 Computer Use: The First AI to Beat Humans at Desktop Tasks
GPT-5.4 scored 75% on OSWorld-Verified, surpassing human baseline of 72.4%. Here's what this means for developers building agentic systems in 2026, with real API examples and architectural patterns.
Building a Claude Agent Team: Skills, Collaboration & Delivering Products from Idea to Go-Live (Part 13)
How to build a coordinated Claude agent team: defining agent skills, assigning responsibilities, orchestrating multi-agent workflows, and collaborating across agents to deliver a real software product from idea to production.
Production Voice AI for Research at Scale: Multi-Phase State Machines
Research interviews follow structured protocols with distinct phases. How to build an LLM-driven state machine with next_phase() function calling and dynamic instruction swapping via set_chat_ctx().
The Voice AI Interview Playbook: Multi-Role Agents — Interviewer, Coach, and Evaluator Personas
Three personas, one infrastructure. How to build an AI interviewer that asks questions, a coach that gives feedback, and an evaluator that scores fairly — all with system prompts, function calling, and state machines.
I Spent a Weekend Building AI Agents. Most of Them Were Useless.
I experimented with CrewAI and LangGraph to build multi-agent workflows. Some were genuinely useful, some were expensive toys. Here's what I learned.
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