Technology Evolution Doesn’t Move in a Straight Line—It Spirals

The Proud Ops Colleague

Years ago, an Ops colleague proudly showed me something new. ClusterSSH—cssh [1]. A tool that opens multiple terminals to multiple machines—at the same time. You type once, it executes everywhere.

Back then, machines still had names. Ops folks knew their history, their specs, their quirks. They could tell you which server had been acting up last Thursday and what firmware it was running. And cssh? It let them follow the runbook consistently across every node. No more SSH-ing into machines one by one, hoping you didn’t forget a step on node 7.

𝗠𝗖𝗣 𝗧𝗼𝗼𝗹 𝗖𝗵𝗮𝗼𝘀 - 𝗴𝗼𝘁 𝗹𝗼𝘀𝘁 𝗶𝗻 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲?!

Agentic coding has become reality for developers everywhere 🚀. Tools like Anthropic’s Claude Code and Amazon’s Kiro are leading the charge, boosting coding efficiency and developer experience—as long as you stay firmly in the driver’s seat. But with great power comes challenges, especially as agentic workflows drive more tool integrations via the Model Context Protocol (MCP). Developers and teams now juggle multiple MCP servers, each with its own endpoints, authentication flows, and security requirements. This raises key issues: How do we guarantee security and compliance at scale, whether for solo devs or enterprise teams? And from a pure DX perspective, who wants to wrangle endless auth methods? 😩

🎯 “How do we pick the RIGHT AI agent use case?

This is the question I hear most from customers exploring agentic AI.

Here’s the mechanism I run through together with the customer:

The 4-Quadrant Evaluation

When a customer brings me 5-10 agent ideas, we structure each one across four dimensions:

📊 Business Value & Strategic Fit → What pain does it solve? For whom? How often? → Can we quantify the impact? (Revenue, cost, time, quality) → Which KPI moves if this works for 6 months?

Kiro Subagents: Scaling Development with Specialized AI Agents

When you’re building complex software, context management becomes your bottleneck. Your AI agent is juggling frontend components, backend APIs, database schemas, testing frameworks, and documentation—all competing for limited context window space. The result? Diluted focus and suboptimal outputs.

Kiro Subagents solve this architectural challenge by enabling parallel task execution through specialized, autonomous agents that maintain independent context windows.

🏗️ The Architecture: Parallel Contexts, Focused Execution

Subagents operate as independent processes with their own context management. This architectural pattern delivers several technical advantages:

🎯 From Chaos to Control: Building Predictable AI Agents That Get Smarter Over Time

⚖️ We need to balance Agency versus Control. We want AI systems to be super easy to use, read our minds, and just provide the answer we need. But we also need to make sure that nothing goes wrong. The more we control, the less agency we get. This is a balancing act.

Let’s focus on the control part. There are many different mechanisms to increase and guarantee control. Things like policies and guardrails come to mind. Those are obvious and powerful. I will cover them in a dedicated post.