Observing Agent Behavior at Every Step

Cascading errors without visibility, untracked costs, monitoring that can’t see LLM failures. What token-level tracing solves.

Cascading errors with no visibility

Agent reliability compounds multiplicatively. If each step in an agent has 97% accuracy, ten steps yield roughly 74% overall accuracy (0.97^10). At fifty steps, that drops to about 22%.

Vellum AI describes the failure mode:

“AI agents don’t fail in obvious ways. Instead of crashing or throwing clear errors, they often make subtle mistakes that compound over time.”

A Hacker News commenter described the cascade pattern:

“If it screws something up it’s highly prone to repeating that mistake. It then makes a bad fix that propagates two more errors.”

Most agent frameworks have no step-level tracing. When a 50-step run produces a wrong result, there is no way to replay the execution and identify which step introduced the error. The agent’s reasoning at each step — what it saw, what it decided, why — is lost. Debugging means re-running the agent and hoping to observe the failure in real time.

Cost and quality are untracked

Agent loops consume tokens at rates that are difficult to predict and impossible to attribute without structured metadata.

One developer described the spend: “burned through $638” in one month, with “the LLM using 50k tokens exploring dead ends before finding a solution expressible in 5k tokens.” CodeAnt AI estimates that 30% of tokens in typical agent deployments are wasted on unproductive reasoning paths.

Production costs routinely reach 10x prototype budgets because prototype usage patterns — short sessions, simple tasks — do not predict production patterns — long sessions, complex multi-step workflows, retry loops. Cost attribution is “nearly impossible” without structured metadata at the inference level (Portkey).

Without per-step token tracking, teams cannot answer basic questions: which steps are expensive, which model calls are retries, where the cost ceiling should be. Budget enforcement requires data that most frameworks do not capture.

Traditional monitoring is blind to LLM failures

An API call returns HTTP 200. The response body contains a confident, well-structured, completely hallucinated answer. Traditional application performance monitoring (APM) sees a successful request.

This is the fundamental observability gap for LLM-powered systems. Status codes, latency percentiles, and error rates — the standard signals — cannot distinguish between a correct response and a hallucinated one. The model did not fail. It produced output that is wrong.

Alert fatigue compounds the problem. Grafana’s 2025 Observability Survey found that alert fatigue is the #1 obstacle to incident response, outweighing the next factor by 2:1. Logz.io reports that 82% of organizations have mean time to resolution (MTTR) exceeding one hour, worsening year over year.

The average observability stack uses 8 tools. Over 90% of stored telemetry is never read (Matt Klein). Adding more infrastructure does not help if the infrastructure cannot see the failure mode. LLM quality regressions require domain-specific signals — eval scores, token-level attributes, semantic comparisons — that APM was never designed to capture.

What MIST does

MIST provides the tracing and alerting infrastructure purpose-built for LLM inference.

MTTP traces every step with token counts, model, cost, and latency. Each inference call in an agent loop emits a structured trace with all the metadata needed for replay, attribution, and cost analysis. No custom instrumentation required.

trace.alert messages fire on quality, cost, and latency regressions — not raw thresholds. Alerts are triggered by changes in eval scores or cost patterns, not by static values. A 200ms response that hallucinates triggers an alert. A 2s response that is correct does not.

Transport-agnostic output. Traces go to files during development, HTTP endpoints in production, or stdout for debugging. The same tracing code works in every environment. No collector, exporter, or backend required.

Zero dependencies. MIST adds no runtime dependencies to your application. No agent framework, no SDK initialization, no background processes. Import the package and emit traces.

What MIST does not do

MIST does not provide an agent framework. It does not manage prompts. It does not include a guardrails library.

You build the agent. MIST traces every inference call so you can see what happened, alerts when quality degrades so you know immediately, and tracks costs so you can set and enforce budgets. The agent logic is yours. The observability is MIST’s.