Observability
Flow #4: Logical monitoring of what happened. Every action the system takes gets recorded, measured, and evaluated. Observability is how you know whether things are working — and how you find out when they stop.
What This Flow Does
Observability captures what your AI system did, how well it did it, and what it cost. Every agent decision, every tool call, every output gets logged and measured.
Decision Logging
Every action an agent takes gets recorded with full context — what it decided, why it decided it, and what happened next. When something goes wrong at 2am, you need the trace. Decision logs give you the trace.
Quality Evaluation
Evals run against outputs to measure whether the system is producing good results. Not just "did it respond" but "was the response accurate, useful, and consistent." Without evals, you're hoping. With them, you're measuring.
Performance Monitoring
Latency, throughput, error rates, and availability. AI systems have unique failure modes — a model can return HTTP 200 while confidently producing nonsense. Traditional uptime checks miss the failures that matter most.
Cost Tracking
Token usage, API spend, and compute costs broken down by task, user, and model. Agent loops and large context windows can burn through budgets fast. Granular cost tracking catches the spiral before it hits your invoice.
How It Differs from Safety
This is the distinction that trips people up the most: observability watches and reports. Safety constrains and prevents. You need both, but they do fundamentally different things.
Observability is the rearview mirror. It tells you what happened, when it happened, and how well it went. It surfaces patterns, flags anomalies, and gives you the data to make decisions about what to change.
Safety is the seatbelt. It stops bad things from happening in the first place. It blocks dangerous outputs, enforces boundaries, and escalates when something is about to go wrong.
Here is the practical difference: observability might tell you that an agent made a poor decision 47 times last week. Safety is the layer that would have caught and blocked those decisions before they reached the user. Observability helps you improve the system over time. Safety protects people right now.
A system with great observability but no safety can tell you exactly how it failed. A system with great safety but no observability prevents failures but can't tell you whether it's actually performing well. You need both layers working together — safety as your active protection, observability as your feedback loop.
What This Flow Doesn't Solve
Observability has clear limits, and being honest about them matters more than overselling the concept.
- It doesn't prevent bad outcomes. Knowing that something went wrong is valuable, but it doesn't stop the wrong thing from happening. Prevention is the job of the safety flow (Flow #5). Observability tells you after the fact. Safety intervenes in real time.
- It doesn't store its own data. Logs, metrics, eval results, and traces all need to live somewhere durable and queryable. That's the responsibility of the storage flow (Flow #6). Without proper storage, your observability data disappears or becomes unsearchable.
- It doesn't fix problems automatically. Observability surfaces issues. Acting on them — whether that means adjusting prompts, changing models, or redesigning workflows — is still a human decision. Dashboards don't fix themselves.
- It doesn't replace testing. Evals catch regressions and measure quality in production, but they don't replace proper pre-deployment testing. Observability is your production feedback loop, not your only quality gate.
The goal is clear-eyed visibility into what the system is doing. Not more than that, not less.
Related Building Block
The observability flow draws on specific tools and patterns. For a deeper look at the technical components — eval frameworks, logging infrastructure, monitoring tooling, and cost tracking systems — see the dedicated building block page.
Observability Building Block
Eval frameworks, decision logging, performance monitoring, and cost tracking tooling for production AI systems.
Explore the building block →Need help building observability into your AI system?
I help teams set up logging, evals, and cost tracking that actually get used — practical monitoring that fits your system, not a generic dashboard nobody checks.