Field notes
Production AI patterns we ship every week. Stuck pilots, MCP hardening, eval suites, agent orchestration, cost control. Written by Alex Cinovoj, Founder and CTO of TechTide AI. No vendor pitches, no recycled blog posts.
10 of 22 field notes · 2026 archive
Cancelled agentic projects are rarely killed by the model. They are killed by unscoped ambition, no owner, no evals, no cost ceiling, and a use case that never needed an agent.
MCP is vertical, model to tools. A2A is horizontal, agent to agent. Most teams need the first and adopt the second too early. The decision rule, with failure modes.
Model swaps stopped moving the needle. The gains now come from the harness around the model: durable state, checkpoints, retry policy, budget ceilings, and clean recovery.
The agent does not need to be right every time. It needs to know when it is out of depth. Four escalation triggers, plus the handoff payload that makes a human fast.
Filtering instructions out of untrusted text is a losing game. The defenses that hold are boring: least-privilege tools, typed arguments, allowlists, and approval on writes.
Swarms demo well and page you at 2am. Three orchestration patterns survive real traffic, and the choice is decided by failure isolation, not elegance.
Debugging an agent from a chat transcript is guesswork. Log the full run as a trace, keep cost and latency on the same timeline, and alert on tool-level failures.
Teams argue about whether the agent should be autonomous. Wrong unit. Set autonomy per action, using reversibility and blast radius, and earn each step up with evidence.
Fourteen nodes in a state graph to answer refund questions. Size the stack to the problem: one loop, typed tools, a log, and only then a framework.
The prompt is the smallest part. Production quality comes from context assembly: retrieval scope, ordering, compaction, tool results, and a hard token budget.
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