What Is AI Observability? A Platform Buyer's Guide

TL;DR

• AI observability records every model call, retrieval step, and tool invocation in an AI application, then scores the output for quality rather than only confirming that the service responded.
• Traditional monitoring cannot detect an AI failure on its own: an LLM application can return HTTP 200 in 400 milliseconds and still produce a fabricated refund policy.
• Agentic failures rarely appear in a single call, so a platform has to model sessions, traces, spans, generations, retrievals, and tool calls as distinct entities.
• Eight criteria separate platforms once an agent is in production: trace granularity, online evaluation, human review, dataset curation, OpenTelemetry compatibility, alerting, cross-functional access, and deployment control.
• Maxim AI treats observability as one stage of a lifecycle that also covers experimentation, simulation, and evaluation, so production findings feed back into test suites instead of stopping at a dashboard.

AI observability is the practice of instrumenting an AI application so that every model call, retrieval step, and tool invocation is recorded, scored for quality, and traceable back to a specific user session. Traditional application monitoring answers whether a service returned a response; AI observability answers whether that response was correct, grounded, safe, and worth the tokens it cost. The distinction matters because an LLM application can return HTTP 200 in 400 milliseconds and still produce a fabricated refund policy.

Maxim AI's observability suite addresses this directly, combining distributed tracing for agentic systems with automated evaluation that runs on live production traffic. This guide covers what the practice includes, how it differs from monitoring, and the criteria to apply when evaluating a platform.

What Is AI Observability?

AI observability is the collection and analysis of telemetry from AI systems (prompts, completions, retrieved context, tool calls, token counts, latency, and quality scores) to determine whether the system is producing correct and safe output in production. It extends the standard logs, metrics, and traces model with evaluation signals that measure output quality rather than system health. This shift from infrastructure signals to output signals is what takes a system from a black box to a glass box.

The telemetry spans several layers of a typical AI application:

• Application layer: user inputs, session metadata, explicit feedback such as thumbs-up and thumbs-down ratings
• Orchestration layer: agent steps, branching logic, retries, tool selection, and the sequence in which they executed
• Retrieval layer: queries issued to vector stores or search indexes, documents returned, and whether the answer was grounded in them
• Model layer: prompts, completions, model version, parameters, input and output token counts, and time to first token

Adoption of dedicated tooling is accelerating. Gartner predicts that 40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias, and outputs. For a foundational treatment of the category, see AI observability explained: what it is and how it works.

How AI Observability Differs From Traditional Monitoring

Traditional application performance monitoring assumes deterministic software: the same input produces the same output through the same code path, so threshold-based alerting on error rates and latency is sufficient. LLM applications are probabilistic, which means a run can be fully healthy by infrastructure metrics and still be wrong by product metrics. LLM observability, the model-layer subset of AI observability, adds a second layer of signal on top of the first, measuring the content of the response rather than the health of the service that returned it.

The practical consequence for agentic systems is that failures rarely appear at the level of a single call. An agent can make eight correct model calls and one incorrect tool selection at step three, and every downstream step inherits the error. A platform that scores individual LLM calls in isolation will not surface that pattern. Observability-driven development with distributed tracing covers how teams structure multi-agent systems so those failures stay locatable.

What an Observability Platform Captures for AI Agents

An LLM observability platform records a hierarchical view of each interaction rather than a flat log line. In Maxim, that hierarchy runs from sessions down to individual components, so a multi-turn conversation and a single retrieval call are both addressable units of analysis. The tracing model organizes production data into log repositories, with each repository holding its own logs, metrics, and evaluation results. The modern AI observability stack walks through how agent tracing fits the wider stack.

The elements a platform should capture include:

• Sessions: complete multi-turn conversations, treated as one unit rather than a series of unrelated requests
• Traces: single end-to-end interactions, from user input to final response
• Spans: discrete steps inside a trace, including generations, retrievals, and tool calls
• Generations: individual model invocations with prompt, completion, model, parameters, and token usage
• Metrics: latency, error rate, token consumption, and cost, aggregated over any time window
• Quality scores: evaluator results attached to the session, trace, or span they describe
• Feedback: user ratings and human annotations, stored alongside the trace that produced them

Eight Criteria for Evaluating AI Observability Platforms

The criteria that separate these platforms are evaluation depth, tracing granularity, data portability, and operational control. Feature checklists converge quickly; the differences show up six months into production, when the agent is multi-step, the trace volume is high, and a quality regression needs a root cause within hours. The underlying mechanics of AI observability are worth understanding before applying these criteria, and platform comparisons focused on cost control cover the budget dimension this list treats only briefly.

1. Trace granularity and agent awareness: The platform should model sessions, traces, spans, generations, retrievals, and tool calls as distinct entities. Systems that only log request and response pairs cannot reconstruct why a multi-step agent failed.
2. Online evaluation on live traffic: Evaluation should run continuously against production logs, not only against offline test suites. Look for AI, programmatic, and statistical evaluators, plus control over sampling rules and filters so evaluation cost stays bounded.
3. Human review in the same workflow: Automated evaluators miss tone, intent, and domain nuance. The platform should support human annotation on production logs, including reviewers outside the engineering team.
4. Dataset curation from production logs: Production traffic is the highest-value source of test data. A platform should let teams select real logs, map them to dataset columns, and reuse them for regression testing and fine-tuning.
5. OpenTelemetry compatibility: The OpenTelemetry GenAI semantic conventions define a standard schema for spans, metrics, and events across GenAI systems. A platform that ingests and emits OTLP keeps telemetry portable instead of locking it into a proprietary span format.
6. Alerting connected to on-call: Quality regressions need to reach the same channels as infrastructure incidents. Verify that alerts cover both performance metrics and evaluator scores, and that they route into Slack, PagerDuty, or an equivalent destination.
7. Cross-functional access: Product managers, QA engineers, and domain experts often identify quality problems before engineers do, which is why no-code evaluator configuration matters as much as SDK coverage. A platform that requires code changes for every evaluator configuration puts engineering in the critical path for all quality work.
8. Deployment model and data control: Prompts and completions frequently contain regulated data. Confirm what deployment options exist (managed, hybrid, or fully isolated), how long raw logs are retained, and whether SSO and role-based access control are available.

How Maxim AI Approaches AI Observability

Maxim AI treats observability as one stage of a single lifecycle that also includes experimentation, simulation, and evaluation, so production findings feed directly back into test suites rather than sitting in a separate tool.

Maxim's approach covers the criteria above as follows:

• Distributed tracing built for GenAI: Production data lands in log repositories that can be split by application, service, or environment. The architecture uses idempotent commit logs so trace timelines stay accurate regardless of log arrival order, and it has been production-proven across more than a billion indexed logs.
• Multi-level online evaluation: Maxim runs online evaluations at session, trace, and span level. Session-level evaluators assess whole conversations, trace-level evaluators score single interactions, and span-level evaluators target individual generations, retrievals, or tool calls. Evaluation runs against filtered, sampled subsets of logs so teams control the cost.
• Human annotation alongside automation: Human evaluators can be configured on a log repository and used in-app by the team, or extended to external raters through an email-based workflow. The same evaluator definitions apply in test runs and in production, which keeps offline and online scores comparable.
• Dataset curation from production: Teams select logs from a repository, map log fields to dataset columns, and curate datasets for regression testing, evaluation, and fine-tuning. This closes the loop between what happened in production and what gets tested before the next release.
• OpenTelemetry ingestion and forwarding: Maxim accepts OTLP traces over HTTP in Protobuf or JSON and maps supported semantic convention attributes into its own traces, spans, generations, and tool calls. Data connectors forward the same enriched traces onward to New Relic, Snowflake, or any OTLP collector, so one instrumentation pass serves multiple destinations.
• Alerting on quality and performance: Alerts fire on latency, token usage, and cost thresholds as well as evaluator scores, and deliver through Slack or PagerDuty integrations.
• Enterprise deployment options: Maxim supports self-hosting with either full VPC isolation or a hybrid data plane deployment inside the customer's cloud environment, alongside workspace-level role-based access control.

Pointing existing OpenTelemetry instrumentation at Maxim is a change of endpoint and headers rather than a re-instrumentation:

OTEL_EXPORTER_OTLP_ENDPOINT="https://api.getmaxim.ai/v1/otel"
OTEL_EXPORTER_OTLP_HEADERS="x-maxim-api-key=<API_KEY>,x-maxim-repo-id=<REPO_ID>"
OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf"

Questions to Ask Before You Buy

Does the platform evaluate at more than one level of granularity?

Session, trace, and span-level evaluation answer different questions. Session scores tell you whether a conversation succeeded; span scores tell you which retrieval or tool call caused the failure. A platform limited to one level will identify problems without localizing them.

Can production traces become evaluation datasets without manual export?

If curating a test set requires exporting CSVs and reimporting them elsewhere, the improvement loop slows to the speed of that manual step. Ask to see the path from a failing production log to a dataset entry in the vendor's own UI.

Is the telemetry portable if you switch platforms?

OTLP ingestion and OTLP forwarding are the practical tests. A platform that accepts standard GenAI spans and can forward them to another collector leaves the option open; a proprietary-only SDK does not.

Who besides engineers can configure evaluations?

Ask which evaluator configurations are available in the UI and which require SDK changes. This determines whether product and QA teams can operate independently or file tickets.

What happens to prompts and completions at rest?

Confirm retention periods, deployment topology, and access controls before instrumenting anything that touches regulated data. Production-ready AI observability and monitoring covers the operational side of that decision. Gartner also expects explainability requirements to push LLM observability investment to 50% of GenAI deployments by 2028, up from 15% today, which makes auditability a procurement question rather than an engineering preference.

Can you evaluate production traffic without scoring every request?

Continuous evaluation on full traffic is expensive at volume. Ask whether evaluators can run against filtered, sampled subsets of logs, and whether those rules are configurable per repository. Maxim runs online evaluations against sampled and filtered logs for exactly this reason, so evaluation coverage and evaluation cost are separate decisions.

Does OpenTelemetry support remove the need for a vendor SDK?

Not entirely, and the difference is worth testing. OTLP ingestion covers standard GenAI spans, but platform-specific concepts such as evaluator attachment and dataset curation usually need the vendor's own SDK or UI. Ask which capabilities are available through OTLP alone and which require native instrumentation, then instrument a single agent both ways before committing.

Getting Started with AI Observability

An observability platform earns its place when it shortens the distance between a production failure and a verified fix. That requires tracing granular enough to localize the failure, evaluation continuous enough to catch it without a user report, and data curation direct enough that the failure becomes a permanent regression test. Maxim AI covers those three requirements in one platform, with the deployment and access controls that regulated teams need. Teams comparing approaches more broadly can review how observability platforms monitor, trace, and improve LLM-powered applications.

To see how Maxim AI fits your AI observability and evaluation workflow, book a demo or start for free.