AI Observability and LLM Monitoring

Overview

AI Observability monitors the AI systems your organisation runs — large language models (LLMs), AI agents, retrieval-augmented generation (RAG) pipelines, and the applications built on them — tracking performance, cost, output quality, and behaviour in production. AI applications fail in ways traditional monitoring wasn't built to catch: a model can respond quickly and still be wrong, an agent can loop through tool calls and burn budget, and token costs can climb without anyone noticing. Avocado extends your existing observability platforms to cover AI workloads, so AI is monitored with the same rigour as the rest of your stack.

AIOps applies AI to IT operations. AI observability works the other way — applying observability to AI. As organisations move generative and agentic AI from pilot into production, they need answers to new questions: which prompts are driving cost, why an agent took a particular action, whether a model's answers are drifting, and whether guardrails are working. Those questions need new telemetry, connected to the infrastructure and application data you already have.

Who is this for?

You'll recognise the situation

You're moving generative or agentic AI use cases from pilot into production.

AI costs (tokens, API calls, GPU usage) are growing, and you can't see where they're coming from.

You can't explain why an AI agent took a specific action or produced a specific answer.

Risk, compliance, or the board are asking how your AI systems are monitored and governed.

Your AI applications are slow or unreliable, and you can't tell whether the cause is the model, the data, or the infrastructure.

What's included?

Monitored with the same rigour as the rest of your stack

LLM performance and cost monitoring

Track latency, error rates, token usage, and spend by model, application, and use case.

Agent and workflow tracing

Trace each step an agent or chain takes — prompts, tool calls, retrievals, and responses — so behaviour can be explained and debugged.

Output quality and drift

Monitor response quality, hallucination indicators, and model drift over time, with alerting when behaviour changes.

Guardrail and policy monitoring

Track guardrail triggers, prompt injection attempts, and sensitive data appearing in prompts and responses.

RAG pipeline visibility

See how retrieval, vector databases, and source data affect answer quality and response time.

Infrastructure correlation

Connect AI behaviour to the GPUs, services, and cloud resources underneath, so problems are traced to their real cause.

How do we deliver it?

Discover, instrument, govern

AI workload discovery

Identify the models, agents, and AI applications in use, and what visibility exists today.

Instrumentation

Extend Dynatrace, Splunk, or OpenTelemetry instrumentation to capture AI-specific telemetry.

Dashboards and alerting

Build views for engineering, operations, and risk teams, aligned to cost, quality, and performance thresholds.

Governance alignment

Connect monitoring to your AI governance and security controls, so evidence is available for audit and assurance.

Ongoing optimisation

Tune thresholds, track cost and quality trends, and extend coverage as new AI use cases go live.

What tools and technology do we use?

Vendor-neutral AI telemetry

Dynatrace and Splunk AI observability capabilities, with OpenTelemetry's generative AI conventions for vendor-neutral instrumentation of models and agent frameworks — so AI telemetry sits alongside your existing infrastructure and application data rather than in a separate tool.

What outcomes can you expect?

What you walk away with

Clear visibility of AI cost by application and use case.

Faster diagnosis of slow, failing, or misbehaving AI applications.

Evidence to support AI governance, risk, and compliance reporting.

Confidence to scale AI from pilot into production.

How is this engagement structured?

Delivered as a focused engagement for a specific AI application, or as part of a broader observability or AI and Data program. Contact us to discuss the right approach.

Common questions

What is AI observability?

AI observability is the practice of monitoring AI systems in production — LLMs, AI agents, and RAG pipelines — tracking performance, cost, output quality, and behaviour, and connecting that to the underlying infrastructure and application data.

What is LLM observability?

LLM observability is a subset of AI observability focused specifically on large language models: prompts, responses, token usage, latency, and output quality.

What's the difference between AI observability and AIOps?

AI observability monitors AI systems. AIOps uses AI to run IT operations. The names are similar, but they solve different problems, and many organisations eventually need both.

Can we use our existing observability platform?

In most cases, yes. Dynatrace and Splunk both extend to AI workloads, and OpenTelemetry provides a vendor-neutral standard, so AI monitoring can sit alongside your existing data rather than in yet another tool.

How does this support AI governance?

Monitoring provides ongoing evidence that AI systems are behaving as intended — guardrail activity, output quality, and data exposure — which supports governance frameworks such as ISO/IEC 42001 and Australia's Voluntary AI Safety Standard.