Observability

AI Observability in 2025: How to Monitor, Evaluate, and Improve AI Agents in Production

AI Observability in 2025

AI systems have crossed the threshold from prototypes to production-critical infrastructure. Customer support bots resolve thousands of tickets. Document agents triage insurance claims. Voice agents interview candidates in real time. When these systems fail, it impacts user trust, revenue, brand, and compliance. AI observability is how you stay ahead of that risk.

This guide presents a practical, standards-aligned blueprint for AI observability you can deploy today. You will learn how to collect the right telemetry, design online and offline evaluations, route edge cases to human review, trigger alerts that matter, and turn production logs into a compounding data advantage. Throughout, you will find direct links to Maxim’s product capabilities, documentation, and articles so you can implement the same patterns in your stack.

Key Takeaways

Instrument end-to-end traces, then layer online evaluations, human review, and targeted alerts on top.
Measure both session-level outcomes and node-level steps to diagnose quality precisely.
Use simulations before release and continuous online evaluations after release to catch regressions early.
Govern with auditable lineage and align with enterprise standards and AI risk frameworks.
Close the loop by curating datasets from production, scheduling regressions, and reporting version deltas.

What AI Observability Actually Means

At its core, observability is the ability to understand a system’s internal state from its external outputs. In classical SRE practice, teams monitor the Four Golden Signals of latency, traffic, errors, and saturation to detect and triage user-facing problems. See Google SRE’s chapter on Monitoring Distributed Systems for background on signal selection and alert hygiene.

AI observability builds on that base and adds AI-specific layers. These include prompt versions, tool calls, retrieval context, model responses, evaluator scores, human annotations, and safety signals that do not exist in traditional software monitoring. Your goal is not simply a fast endpoint. It is a reliable end-to-end agent that consistently satisfies user intent, stays on task, avoids hallucinations, respects policy and privacy, and handles tool or context failures gracefully.

Maxim’s Observability platform maps directly to these needs. It offers granular distributed tracing for LLM and non-LLM spans, online evaluations to score and evaluate AI responses, human review queues for nuanced cases, and alerting that ties quality signals to Slack and PagerDuty. Pair that with Experimentation for fast iteration and Simulation and Evaluation to test before and after shipping.

Quick Definition: AI observability is the continuous practice of tracing AI workflows end to end, evaluating quality online and offline, routing ambiguous cases to human review, and alerting on user-impacting issues, with a governance loop that curates data and drives measurable improvements over time.

Standards and Governance Context

A mature observability practice should align with recognized frameworks, especially in regulated environments.

NIST’s AI Risk Management Framework (AI RMF) defines four core functions for trustworthy AI: Govern, Map, Measure, and Manage. Observability and evaluations directly support Measure and Manage, while traceability and human review support Govern.
ISO/IEC 42001 is the first AI Management System standard. It emphasizes leadership, risk identification, operational controls, performance evaluation, and continual improvement. Continuous monitoring, quality evaluations, and auditable traces make performance evaluation measurable and repeatable.

Maxim’s enterprise features such as in-VPC deployment, SOC 2 Type 2 posture, role-based access control, SSO, PII management, and custom log retention help operationalize these frameworks while avoiding data sprawl.

Review Pricing and feature tiers.

At a Glance: The Five Pillars

Traces: End-to-end visibility across agent steps and tools
Online Evaluations: Continuous quality scoring on real traffic
Human Review: Targeted annotation for high-stakes and ambiguous cases
Alerts: Real-time, low-noise signals wired to on-call workflows
Data Engine: Curate datasets from production for regression and fine-tuning

The Core Pillars: Traces, Evaluations, Human Review, Alerts, and the Data Engine

1) Traces: See Every Step the Agent Took

Agents are workflows, not single model calls. They retrieve, call tools, branch, and iterate. You need end-to-end, multi-span traces that capture:

Inputs and outputs at each node, including model, prompt version, and hyperparameters
Tool calls, arguments, responses, and latencies
Retrieval context provenance and ranking details
Branching decisions, retries, and termination reasons
Cost, token usage, and rate limiting events

Maxim provides comprehensive distributed tracing for both LLM and traditional spans, with a visual trace view that makes branching behavior and tool interactions explicit. It supports larger trace elements, CSV and API exports, and OpenTelemetry compatibility, so you can forward to New Relic or any OTel-based platform. For consistency across polyglot services, standardize trace attributes using OTel’s Trace Semantic Conventions and the Semantic Conventions overview.

Explore: Agent Observability: Traces and Export

2) Online Evaluations: Measure Real-World Quality Continuously

Tracing shows what happened. Evaluations tell you if what happened was good. Online evaluations run on live traffic and assign scores to sessions, spans, and model calls on dimensions such as:

Task success and user intent satisfaction
Faithfulness to retrieved context for RAG
Toxicity, safety, bias, and PII leakage
Format adherence and structured output correctness
Tool call correctness and error recovery

Maxim lets you define sampling rules for which logs are evaluated, choose prebuilt evaluators or bring custom ones, and store scores alongside your traces. You can set alerts on evaluator scores and route problematic sessions to human review queues. This creates a continuous feedback loop that catches regressions early and reduces mean time to detect quality issues.

Explore: Agent Observability: Online Evaluations and Platform Overview. For more depth, see the Observability articles.

3) Human Annotations: The Last Mile of Quality

Automated evaluations do most of the work at scale, but high-stakes decisions and nuanced edge cases still need human judgment. A functional human-in-the-loop pipeline should support:

Auto-creating review queues based on rules such as low faithfulness, negative user feedback, or suspected PII
Multi-dimensional rubrics tailored to your domain
Internal or external raters with quality controls and inter-rater reliability checks
Clear escalation paths back to engineering with deep links to traces

Maxim’s human annotation features enable these workflows and integrate with the same observability surface your engineers use, so nothing lives in a silo.

Explore: Human Annotation and Review Queues

4) Real-Time Alerts: Signal Over Noise

Alert fatigue kills reliability programs. Alert on the few things that truly require a human at 3 am, and push the rest to ticket queues or dashboards. The Four Golden Signals still apply for infrastructure, but you also want AI-native quality thresholds:

Latency and error rates at the session and tool-call levels
Cost per request and cost per resolved task
Evaluator thresholds for faithfulness, policy compliance, and safety
Spike detection for tool failures and retrieval outages
Degradation in success rates for key workflows, broken down by persona, language, or channel

Maxim integrates with Slack and PagerDuty so you can target the right team with the right context, including links to traces and recent evaluation trends.

Explore: Real-time Alerts and Notifications

5) The Data Engine: Turn Production Logs into a Compounding Advantage

Your best datasets are mined from production. With the right pipeline, you can continuously curate evaluations, fine-tuning corpora, and test suites:

Capture representative traffic with privacy-safe logging and masking
Auto-label subsets with online evaluators and human reviewers
Cluster by failure modes and personas
Promote curated sets into your evaluator store and regression tests
Track dataset lineage and versioning for auditability

Maxim’s Data Engine connects observe and evaluate so your system improves every week, not just after one-off fine-tuning.

Explore: Platform Overview: Data Engine

Session-Level and Node-Level: Measure the Right Layers

Agents are multi-turn, multi-tool workflows. You need both:

Session-level metrics: task success, resolution time, back-and-forth turns, cost per resolved task, user satisfaction
Node-level metrics: retrieval recall and precision, tool call correctness, parsing accuracy, guardrail triggers, branching quality, retry success

See: Session-Level vs Node-Level Metrics

Online and Offline Evaluations: When and Why

You need both evaluation modes working in tandem.

Online evaluations measure real-world behavior in production. They catch regressions, drift, and unexpected edge cases. They power alerts and feed the data engine with high-impact examples.
Offline evaluations measure candidate prompts, models, and workflows against consistent test suites. They are your pre-deployment safety net and support A/B decisions with evidence.

Maxim provides unified facilities for both with automation hooks for CI and dashboards for version comparisons. For a deeper dive, see Agent Evaluation vs Model Evaluation and the Platform Overview.

Agent vs Model Evaluation: Three Key DifferencesObject of Measurement: Agents measure end-to-end task success across steps and tools. Models measure single-turn outputs.Metrics: Agents use session and node metrics like success rate, faithfulness, tool correctness. Models use accuracy, BLEU, F1, or rubric-based LLM-judged scores.Failure Diagnosis: Agents localize failures to specific nodes or tools via traces. Models localize to prompt or data issues in isolation.

A Reference Architecture for AI Observability

Adopt this blueprint quickly.

Instrumentation

Standardize on OpenTelemetry across services and agent orchestration for HTTP, DB, tool calls, and LLM spans using Trace Semantic Conventions.
Use Maxim’s stateless SDKs for tracing, online evaluations, and log export. See Agent Observability.

Quality Dimensions and Evaluators

Define a minimal evaluator bundle per product surface. For a RAG assistant: Task Success, Faithfulness, Toxicity, PII leakage, and Format adherence.
Start with prebuilt evaluators and add custom ones as your maturity increases. See Platform Overview.

Sampling and Evaluation Strategy

Start with 5 to 10 percent sampled sessions per surface for online evaluations, with higher rates for new versions and high-risk routes.
Auto-route low-scoring sessions to human review with clear rubrics and SLAs.

Alerts and SLOs

Define SLOs around user outcomes and response quality, not just latency. Consider success rate, tail latency, faithfulness, and cost budgets per task type.
Integrate alerts with Slack or PagerDuty and include deep links to traces. See Agent Observability.
Anchor infrastructure alerts to the Four Golden Signals and enrich with AI-native evaluator thresholds.

Datasets and Regression Loops

Promote reviewed examples into curated datasets. Label by scenario, persona, and failure mode.
Run scheduled offline regression evaluations on nightly builds and on every major prompt or model change.
Report deltas across versions in comparison dashboards, and publish a weekly reliability digest. See Platform Overview.

Governance and Auditability

Maintain lineage from production log to dataset to evaluation to deployment decision to incident review.
Align processes with NIST AI RMF’s Measure and Manage functions and track maturity over time. See NIST AI RMF.
For ISO/IEC 42001 readiness, document your monitoring plan, evaluation cadence, and continual improvement process using this ISO 42001 overview.

What to Monitor in Production: A Practical Checklist

Quality and Safety
- Task success rate and failure taxonomies
- Faithfulness to context for RAG flows
- Policy compliance: toxicity, harassment, bias, safety
- PII detection and redaction effectiveness
- Output validity: schema adherence and JSON parsing correctness
Tooling and Retrieval
- Tool call success and retry rates
- Retrieval hit rate, context overlap, and latency
- Backoff behavior and circuit breaker activations
User Experience
- End-to-end latency by percentile and persona
- Turns per resolution and abandonment rate
- Escalation to human and time to resolution
Cost and Performance
- Token usage per step and per session
- Cost per resolved task and per failure mode
- Rate limiting and provider error distributions
Infrastructure and Golden Signals
- Errors, latency, traffic, and saturation at APIs and microservices
- Dependency timeouts and downstream saturation indicators

Maxim’s online evaluations and alerts attach directly to these metrics so your dashboards and notifications are tied to what matters for users and the business. Explore Agent Observability.

Observability-Driven Development

Bake observability into your development lifecycle.

Run every change against a representative offline test suite in Maxim with prebuilt and custom evaluators.
Increase online sampling for each new version until quality stabilizes.
Auto-open tickets for regressions with trace links, and cluster similar issues to remove duplicated work.
Pull reliability projects from failure-mode clusters mined from production.
Use unified reports to track cost, latency, and safety in product and compliance reviews.

Maxim’s Experimentation capabilities pair naturally with this flow. Prompt versioning, side-by-side comparisons, bulk test runs, and SDK-based deployments decouple prompt iteration from code pushes.

Simulation Before You Ship

Production is not a safe place to discover basic failure modes. Simulation helps you uncover them early. With multi-turn AI-powered simulations, you can:

Test complex scenarios and user personas that mirror real traffic
Exercise tool-calling logic through chained tasks
Stress-test branching and recovery behavior
Generate synthetic datasets that complement your production corpus mimicking real-world scenarios

Run simulation and evaluation before deployment to reduce the blast radius of changes and create a safety net for workflows with high variance.

Explore: Agent Simulation and Evaluation and the guide on Agent Simulation in Realistic Conditions

Getting Started in One Week

Day 1: Define Quality Dimensions and Evaluators
Pick 3 to 5 evaluators aligned with your product goals. For a RAG support bot, start with Task Success, Faithfulness, Toxicity, and Schema Validity. Map current prompts and agent workflows in Experimentation.

Day 2: Instrument Tracing and Deploy Sampling
Install Maxim’s SDK into the orchestration layer. Standardize attributes using OTel’s Trace Semantic Conventions. Turn on 10 percent sampling for online evaluations on core routes.

Day 3: Stand Up Dashboards and Alerts
Create dashboards for session outcomes, node failures, and cost per resolution. Add alerts on evaluator thresholds and golden signals for core APIs. Use Slack and PagerDuty integrations in Agent Observability.

Day 4: Human Review Queues
Define routing rules to send low-faithfulness or PII-flagged sessions to human review. Set reviewer SLAs and rubrics. Close the loop by filing issues with trace links.

Day 5: Curate Datasets and Schedule Regression Evaluations
Export reviewed sessions into a curated dataset and set nightly offline evaluation runs in Maxim. Establish a weekly reliability report comparing versions, highlighting top failure modes, and recommending fixes. See Platform Overview.

PM Playbook: SLOs, Release Checklist, and Business Metrics

SLOs to Track

Success rate by surface and persona
P95 and P99 end-to-end latency
Faithfulness score for RAG
Cost per resolution and budget adherence

Release Decision Checklist

Offline regressions pass with target thresholds
Online sampling ramp plan defined with rollback triggers
No critical alert spikes in the last 24 hours
Evaluator threshold alerts active and tuned
Human review rubrics ready for expected edge cases
On-call ownership and escalation paths confirmed

Business Metrics Mapping

CSAT and containment rate trends
Average handle time and abandonment rate
Cost per ticket and deflection percentage

FAQ

What Is AI Observability?
AI observability is the continuous practice of tracing agent workflows, evaluating quality online and offline, routing ambiguous cases to human review, and alerting on user-impacting issues, with a governance loop that curates data and drives measurable improvements over time.

How Do Online Evaluations Differ from Offline Evaluations?
Online evaluations score live traffic and catch regressions, drift, and real-world edge cases. Offline evaluations score proposed changes against stable test suites before deployment. You need both to move fast without breaking quality.

How Do I Use OpenTelemetry with LLM Agents?
Instrument your orchestration layer and tools with OTel spans using the standard Trace Semantic Conventions. Include attributes for prompts, tool calls, retrieval metadata, costs, and errors. Export to Maxim for analysis and optionally forward to your existing OTel ecosystem.

What Metrics Should I Monitor for RAG Faithfulness?
Monitor faithfulness scores, context retrieval, and response hallucination flags. Track these at node level and correlate to session-level success rates and user feedback.

How Do I Set Alerts for Agent Quality?
Start with evaluator thresholds for success and faithfulness, plus safety and PII flags. Add cost per resolution budgets and tool failure spike detection. Route incidents to Slack or PagerDuty with trace links for fast triage using Agent Observability.

The Bottom Line

AI observability is not about just building a dashboard or looking at system logs. It is a discipline that connects traces, evaluations, human judgment, alerting, and data curation into a tight loop of continuous improvement. Start with high-fidelity traces and a minimal set of evaluators. Wire alerts to real user outcomes, not just infrastructure metrics. Route ambiguous cases to human review or llm as a judge evaluators and promote the best examples into your datasets. With that loop in place, every week of production makes your agent smarter and more reliable.

Maxim gives you this loop end to end. Use Experimentation to iterate safely, Simulation and Evaluation to test before you ship, and Agent Observability to monitor, evaluate, and improve continuously in production.

If you are interested, review our Pricing or request a demo.