# AI agent observability, every step on the record. Every Arclyx run is recorded step by step: what the agent decided, each tool call and what came back, the tokens it used and what it cost. Each agent's page charts its run history, and agents that need attention are flagged on your home page, so you see a problem before someone tells you about it. ## Why do AI agents need observability? Because a wrong answer reads exactly like a right one. The only way to know an agent did the job properly is to see how it got there: what it searched, what it read and what it skipped. And when it runs on its own, the record is the only witness. - Without it: You see the answer, not how it got there - Without it: A slow or costly agent hides in the total - Without it: You hear about a broken run from a colleague - With Arclyx: Every step and tool call, in order - With Arclyx: Tokens and cost for each run - With Arclyx: A Needs you flag the moment one stops short ## What does Arclyx record for every agent run? The whole run, live while it happens and kept afterwards. - **Every step**: Each round: what the agent decided and what it did next. - **Every tool call**: The tool, what it asked for and what came back. - **Tokens**: Input, cached, output and reasoning, for the run and for each model call. - **Cost**: The run's price and a line for every call, on paid plans. - **Helpers**: When an agent hands work to helpers, their runs are recorded too. ## What does a recorded run look like in Arclyx? Every run has its own page: what happened in a sentence, the numbers, a round-by-round replay and the model calls behind it. The agent's page charts all its runs together. ## What should you trace in an AI agent? Everything between the request and the answer. A run can finish without an error and still have called the wrong tool or read the wrong data. These are the parts of a trace that catch it, and Arclyx records each one. - **Each decision**: What the agent chose to do in each round, and what it did next. - **Each tool call, with its input**: The tool, what it asked for and what came back, so a wrong search shows up. - **What it read**: Documents and pages it searched or opened, as tool calls you can open. - **Handoffs to helpers**: The brief and model each helper was given, and the helper's own run. - **Tokens and cost per call**: So one expensive step stands out from the rest. ## How do you spot a problem across many runs? Each agent's page charts its runs as dots, by duration, tokens or price. A run that took twice as long or cost twice as much stands out; click it to open the run. Agents whose last run stopped short show as Needs you on your home page. - **Run history**: Every run as a dot, by duration, tokens or price - **Needs you**: Flagged on Home when a run stops short or a connection breaks - **Runs list**: Every run across all your agents, in one place - **Triggers list**: Every schedule and webhook, with pause and delete ## How do you debug an AI agent run that went wrong? Start from the run, not the answer. Find the round where it went off track, see what the tool gave back, and fix the cause: the instructions, the tools, or a broken connection. Then run it again and compare. - **Find it**: A Needs you flag on Home, or a dot that stands out on the run-history chart - **Open the run**: Every round, tool call and result, in order - **Find the step**: The round where it searched for the wrong thing, got nothing back, or kept going - **Fix it**: Change the agent in plain words with Improve, or reconnect the tool - **Check it**: Run it now, and compare the new run with the old one ## How do you see what your AI agents cost? Settings → Usage shows the last 30 days of spend or calls with a daily chart, and how close you are to each limit. Admins can switch to everyone's usage. Every run's own page shows what that run cost. - **30 days**: Spend or calls, with a daily chart - **Limits**: Meters for each limit you've set - **Everyone**: Admins see usage across the workspace - **Per run**: The price of each run, call by call ## Questions about AI agent observability. ### What is AI agent observability? Seeing what an agent did on every run, not just what it answered: each decision, tool call, result, token and cost, so you can tell whether it did the job properly. ### Is observability the same as monitoring? Monitoring tells you something went wrong; observability lets you see why. Arclyx flags problems and keeps the full record of every run. Our post on the observability gap goes deeper. ### Do I need a separate AI agent observability tool? Not for agents you run in Arclyx. Observability is built in: every run is recorded step by step with nothing to install or set up. ### How is observability different from evaluation? Observability shows what an agent did on real runs. Evaluation scores it against a set of cases. In Arclyx you check a draft with test runs, then watch live runs in their history. ### Can I see what each run cost? Yes, on paid plans each run shows its price and a line for every model call. On Free, runs show tokens and usage shows your share of the allowance. ## Where to go next - [Test runs](https://arclyx.ai/product/test-runs): The same record, before it goes live - [Guardrails](https://arclyx.ai/product/guardrails): Limits enforced on every run - [The Observability Gap](https://arclyx.ai/blog/the-observability-gap): Why agents need more than logs ## See every step your first agent takes. One sentence in, a working agent out. Join the beta and we'll set you up. Join at https://arclyx.ai/#join or write to hello@arclyx.ai.