Observability
Eval, logging, monitoring, and cost tracking. If your AI system is running in production without observability, you're flying blind — and you won't know it until something breaks.
What Observability Covers
Observability is everything that tells you what your AI system is actually doing, how well it's doing it, and how much it costs.
Eval Frameworks
Testing output quality systematically. Evals are how you know whether a model change, prompt tweak, or new context source actually made things better or quietly made them worse. Without evals, you're guessing.
Decision Logging
Capturing every agent action, tool call, and reasoning step. When an agent makes a bad decision at 3am, you need the full trace to understand why. Decision logs are the difference between debugging and guessing.
Performance Monitoring
Latency, throughput, error rates, and uptime. AI systems have failure modes that traditional monitoring doesn't catch — a model that returns 200 OK but gives nonsense answers still looks healthy to your load balancer.
Cost Tracking
Token usage, API costs, and compute spend. AI costs can spiral fast, especially with agent loops and large context windows. Granular cost tracking per task, per user, and per model keeps budgets under control.
Why It Matters
You can't improve what you can't measure. That's true of any software system, but it's especially true of AI — because AI systems fail in ways that are harder to detect. A traditional API either works or it throws an error. An LLM can confidently return plausible-sounding garbage, and your monitoring dashboard will show all green.
Here's what I see go wrong without observability in place:
- A prompt change that seemed fine in testing causes a 30% drop in output quality over two weeks — nobody notices until a client complains.
- An agent enters a retry loop that burns through hundreds of dollars in API calls overnight because there's no cost alerting.
- A RAG pipeline starts returning stale context after an index update fails silently. Outputs degrade gradually and nobody can pinpoint when it started.
- A model provider changes their API behaviour. Responses are technically valid but subtly different — and downstream systems start producing inconsistent results.
- Two teams are running the same queries through different models without realising it, doubling costs for no reason.
Every one of these is preventable with the right observability setup. Not with expensive tooling — with the right logging, the right evals, and the right alerts in the right places.
Pairs With
Observability doesn't work in isolation. It connects to and strengthens several other building blocks.
Safety
Observability verifies that safety boundaries are being respected in practice, not just in theory. Logging every guardrail trigger, every blocked action, and every escalation gives you proof that your safety layer is actually working.
Explore Safety →Agents
Every agent decision gets logged for audit and improvement. When an agent chooses a tool, plans a sequence of steps, or decides to escalate, that trace is what lets you understand behaviour patterns and fix bad ones.
Explore Agents →Storage
Logs, metrics, and eval results need to be stored and queryable. Raw observability data is useless if you can't search it, aggregate it, and trend it over time. The storage layer makes observability actionable.
Explore Storage →Need help building visibility into your AI system?
I help teams set up eval frameworks, logging pipelines, and cost tracking that actually get used — practical observability that fits your system, not a generic dashboard.