Blog
Original research on MCP agent observability, evaluation methodology, and the evolving landscape of AI agent infrastructure.
Default eval thresholds are designed to catch catastrophe, not degradation. Here's how configurable thresholds and smarter rule exclusion turn your evals from rubber stamps into real quality gates.
Output Quality Score (OQS) is a composite metric that rolls completeness, relevance, safety, and cost into one number — giving teams a single quality signal for every agent output.
Static eval thresholds break over time. Self-calibrating eval is the pattern where the system monitors its own scoring distribution and recommends adjustments — always human-approved.
Most teams treat eval as a one-time gate. The real pattern is a continuous loop: score, diagnose, calibrate, re-score. This is the eval loop — and it changes how you build agents.
On-chain actions are irreversible. With 250K daily AI agents on blockchain and $3.4B stolen in 2025, real-time pre-execution eval isn't optional — it's the missing safety layer between agent decision and permanent consequence.
Eval-Driven Development applies TDD principles to AI agents: define eval rules before prompts, iterate on scores, ship when rules pass.
Eval coverage measures the percentage of agent executions that receive evaluation. Most teams are at 0%. Here's why 100% is the only target.
The eval gap is why your AI demo works but production fails. Learn the four mechanisms that create it and how inline evaluation closes it.
Eval drift is the silent degradation of agent quality caused by upstream model changes you can't control. Learn how to detect and prevent it.
The eval tax is the compounding cost of every unscored agent output — in trust, engineering hours, and liability. Here's how to stop paying.
How Iris bridges agent observability and infrastructure monitoring by exporting MCP traces as OpenTelemetry spans to Datadog and Grafana.
Why Sentry and Bugsnag can't detect hallucinations, PII leaks, or prompt injection — and what agent-level error tracking looks like.
MCP observability is following the same adoption curve as APM — and teams without agent-native monitoring will face the same reckoning.
A step-by-step tutorial for evaluating AI agent output using deterministic heuristic rules — no LLM-as-Judge, no added cost, sub-millisecond.
A proposal for standardizing MCP observability with trace schemas, eval interfaces, and cost metadata to prevent ecosystem fragmentation.
Why self-reported agent logs are structurally untrustworthy and how MCP enables architecturally independent observability for AI agents.
How invisible token costs compound to $14,000 monthly bills when agents lack per-trace cost tracking and budget threshold enforcement.
When sub-millisecond heuristic eval rules outperform LLM-as-Judge for PII detection, prompt injection, and cost threshold enforcement.
A comprehensive analysis of the MCP agent observability landscape in 2026, covering market trends, security gaps, and eval approaches.
Learn why traditional APM fails for AI agents and how MCP-native observability with Iris provides the tracing and evaluation agents need.