When an agent does something wrong, the question is always the same: what actually happened. Without traces the answer is reconstruction from logs and memory. We instrument the whole run, every model call, every tool invocation, every retry, with cost and latency attached to each step, so a bad run is something you open and watch rather than argue about.
01 / OBSERVABILITY
For teams who cannot explain what happened. Full execution traces, tool-call success rates, cost and latency per step, and drift over time.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
A log tells you something happened. A trace lets you watch it happen again. On Skionis the validation loop is inspectable step by step, which is why engineers trusted it against a live AWS account.
Per-request cost is an average that hides the one tool call responsible for most of the bill. Step attribution is what makes routing decisions possible later.
The traces feed the eval set, so every production failure is a regression test rather than a story someone remembers.
Week 1
Every model call, tool invocation and retry, with the inputs and outputs that make a run replayable rather than merely counted.
Week 1-2
Per-request totals hide the step that spends the money. Attribution is what turns a bill into a decision.
Week 2-3
Tool-call failure rates and quality drift, not every anomaly. An alert nobody acts on trains everyone to ignore alerts.
Week 3-5
Traces feed the evaluation set: the runs that went wrong become the cases you test against.
Cloud infrastructureIn production
A DevOps agent that turns plain-English infrastructure requests into validated cloud templates and safely applies them to live AWS accounts, planning against real limits and quotas, validating in a loop, and tagging every resource it creates.
ESG & sustainabilityIn production
An ESG-intelligence pipeline that ingests dense, unstructured filings at scale, extracts framework-aligned metrics with full source lineage, connects them across reports and years, and serves them through a governed analytics layer analysts can actually trust.
Fund operationsIn production
An institutional fund and portfolio operations platform. It ingests statements and brokerage data from every custodian, reconciles them automatically, and presents one operational view with the audit and compliance trail regulated finance depends on built in.
Production-proven
Built by engineers who've already shipped this in production.
Usually yes. We emit OpenTelemetry where your tooling accepts it, rather than adding another dashboard nobody opens.
Sampling handles it. Full traces on failures and a sample of successes gives you the debugging value without the volume.
Yes, in what it records. A request duration tells you nothing about why an agent chose the wrong tool. The unit of interest is the decision, not the HTTP call.
Instrument early, alert later. Retrofitting traces onto a system already in production is significantly more work than building them in.
Evals, routing, and audit trails for teams whose demo cannot survive real traffic.
View serviceEval suites and regression testing, for teams shipping on opinion instead of a number.
View serviceRouting across models by task, for teams paying frontier prices for routine work.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.