Since 2010, deploying data systems, ML, and analytics inside enterprises across finance,
telecom, marketing, and credit. PhD trained in economics and organizational psychology.
Now focused on what actually happens when AI hits production: the organizational adaptation,
the verification gap, and the infrastructure that determines whether deployments compound or
decay.
MCP's 2026-07-28 release candidate removes sessions from the protocol. That reads like an argument against persistent agents. It's the opposite: the session that matters was never the protocol's job to hold.
MCP is how agent capabilities travel. It should not be where your operating knowledge lives. For personal agents, the durable leverage is owning the action layer: an operations layer of CLI verbs with stable contracts, progressive discovery, honest errors, and one interface every caller can reuse.
Consciousness is not usually the first mover in moment-to-moment choice. It is a late-binding narrative control layer that converts action into reasons, reasons into identity, and identity into constraints on future action. Locally late. Globally causal.
DORA says the AI productivity dip lasts three months. Telemetry says twelve. That one input swings ROI by $9.9M for a 500-person org. And the curve does not resolve the same way for everyone.
MCP won. In roughly a year, Model Context Protocol went from a clever interoperability idea to the default tool-connectivity layer for AI agents. That makes this a strange time to argue that many agent systems should use MCP less.
2026 is the year the agent protocol stack started to look real. MCP, A2A, ACP, and AGNTCY define how agents communicate. None of them define what a persistent agent is at the infrastructure layer.
The .env file is a collaboration tool for human engineers. It was never meant to be a security primitive. When you run an always-on agent fleet, that distinction stops being theoretical.
Enterprise AI projects don't fail because organizations lack information. They fail because that information exists in a form no system can reliably act on. Curio is built to fix that.
Every agent platform shipping today treats a model call as a request: short-lived, stateless, RPC. Real agents have memory, in-flight tool calls, and partial plans. The request model makes you rebuild context on every call. Sessions are the unit.
Multi-agent systems don't fail because the agents are stupid. They fail because the coordination cost wasn't budgeted for. Identity, overlap, governance, and audit are taxes that compound silently until you get an incident.
Six persistent Claude Code sessions, scoped to different domains, coordinating through EdgePlane. Here's what it looks like in practice.
Every serious attempt to build a multi-agent system converges on the same shape. Then you try to run two workers in parallel, and everything breaks.