See what your AI workflows are doing

We add observability for model calls, retrieval, tool use, workflow state, evaluation results, latency, and spend so production AI issues are debuggable.

Built around your existing telemetry stack where possible, with redaction and retention rules for sensitive prompts and data.

Set up AI observability View agent infrastructure

On-request / scoped service

Agent observability is available only as a scoped platform observability engagement.

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Traces across model, retrieval, and tools

Connect user requests to model calls, retrieved sources, tool invocations, workflow steps, and errors.

Quality and cost signals

Track eval results, groundedness checks, token usage, latency, and spend by workflow and environment.

Safe debugging posture

Define sampling, redaction, retention, and access rules before prompt or document data is captured.

Service playbook

From problem to operating evidence

Main content is structured like a case study: context first, scoped work next, then the operating changes and evidence a team can use after handoff.

Service briefWhat gets instrumentedObservability design choicesQuality signalsDashboards and alerts

Standard application monitoring usually misses the signals that matter in AI systems: prompts, retrieval quality, tool calls, token budgets, model versions, and evaluation results. Agent Observability adds those signals without turning sensitive user data into unmanaged logs.

This service is a fit when you have RAG, assistant, or agent workflows in pilot or production and need to understand regressions, costs, failures, and user trust issues.

Case-study lens

Scoped

Problem, responsibility, and handoff boundaries before implementation.

Evidence

Dashboards, runbooks, reviews, and operating records over borrowed logos.

Outcomes

Conservative summaries focused on observable operational improvement.

EvidenceSection 01

What gets instrumented

Runbooks, dashboards, reviews, and handoff material make the work auditable.

Signal	Captured data
LLM request	Provider, model, prompt/config version, token counts, latency, finish reason, cost estimate, error state
Retrieval	Query, source/index, top results, scores, filters, source freshness, empty-result cases
Tool call	Tool name, allowed action, duration, status, redacted inputs/outputs, retry count
Workflow step	Step name, state transition, checkpoint, timeout, approval status, failure reason
Evaluation run	Dataset/version, score, failed cases, regression status, release gate result
User feedback	Rating, correction, source dispute, escalation, and follow-up owner

EvidenceSection 02

Observability design choices

Reliability signals are treated as decision evidence, not dashboards for their own sake.

Concern	Assistance approach
Sensitive prompts and documents	Define redaction, sampling, retention, and access before collection
Existing telemetry stack	Prefer OpenTelemetry-compatible traces and metrics where practical
Cost attribution	Track spend by workflow, user/team, model, environment, and release
Debuggability	Preserve enough context to reproduce failures without storing unlimited raw content
Governance	Keep audit evidence for model/provider changes, eval gates, and approval checkpoints

OutcomeSection 03

Quality signals

Expected changes are framed as practical operating improvements, not unsupported guarantees.

Observability for AI goes beyond latency and error rate.

What changes

Retrieval quality

precision or relevance checks on representative questions
source freshness and empty-result tracking
unused context detection
citation/source mismatch review
index update and ingestion failure alerts

Operating step

Response quality

structured output validation failures
groundedness checks against retrieved context
refusal and policy-trigger rates
user feedback and escalation trends
prompt/model/config regression checks

What changes

Workflow quality

task completion rate
approval rate and rejection reasons
stuck or cancelled workflow count
tool error rates
cost per successful outcome

EvidenceSection 04

Dashboards and alerts

Reliability signals are treated as decision evidence, not dashboards for their own sake.

Typical dashboards include:

AI request volume by workflow and environment
model latency and token usage
estimated spend by workflow, model, and team
retrieval hit rate and source freshness
eval pass/fail history by release
top failure modes and tool errors

Typical alerts include:

evaluation regression before release
cost spike or budget threshold
retrieval/index freshness failure
tool failure rate increase
p95 latency degradation
repeated approval rejection or stuck workflow

ScopeSection 05

Deliverables

The work is broken into visible capabilities, acceptance points, and handoff artifacts.

Deliverable	Purpose
Telemetry plan	Defines spans, metrics, logs, events, redaction, retention, and ownership
Instrumentation changes	Adds model, retrieval, tool, and workflow signals to the application
Evaluation dashboard	Shows quality and regression status over time
Cost dashboard	Attributes spend by workflow, provider, model, team, and environment
Alert rules	Turns high-risk cost, quality, latency, and tool failures into actionable signals
Runbook	Documents how to investigate and respond to AI incidents

Next stepSection 06

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Next stepSection 07

Getting Started

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Bring one AI workflow, a recent failure or cost concern, and your current telemetry stack. We will map the missing model, retrieval, tool, workflow, and evaluation signals. Set up AI observability →

Ready to get started?

Book a quote review or talk to an engineer.

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Pricing

Flexible scopes available. if you need custom terms or bundled service pricing.

On-request scope

Quoted