Solutions

Whatever shape your agents take, the execution is the unit.

Pick the system you are building. Each section says what to instrument, what JeraViz records and what you would see. These are descriptions of the planned product, not claims about framework integrations.

How instrumentation works

Autonomous AI agents

Agents that plan, act and observe in loops until they decide to stop.

The problem: An agent that loops can burn time and money before anyone notices, and the reason it stopped is rarely logged.
What to instrument: Wrap each plan-act-observe iteration and the final stop decision as spans.
What you would see: Iterations on a timeline, cumulative cost, and the span where the loop ended.
What JeraViz records: Each iteration as a span
Decisions and stop reason
Loop count, cost and duration

Multi-agent workflows

Several agents delegating to one another.

The problem: When agent A hands work to agent B, a failure in B looks like a failure of the whole system.
What to instrument: Record each agent turn as a span and each delegation as a handoff linking the two.
What you would see: Which agent a failure originated in, and latency and cost split per agent.
What JeraViz records: Handoff edges between agents
Per-agent latency and cost
Failure origin across agents

LLM applications

Request, model, response: the simplest and most common shape.

The problem: Model calls are individually simple, but quality, latency and spend drift across prompts and versions.
What to instrument: Record each request with model name and token usage; choose how much text to store.
What you would see: Token, latency and estimated-cost trends per model and per call site.
What JeraViz records: Prompts and completions (capture mode permitting)
Tokens, latency, finish reason
Estimated cost per call

Tool-using agents

Agents that call APIs, databases and functions.

The problem: Agents call tools with arguments you did not write. When one times out, you need the arguments, not a stack trace.
What to instrument: Wrap tool functions so arguments, results, retries and errors are captured.
What you would see: Each tool call with its inputs, output, duration and error category.
What JeraViz records: Tool arguments and results
Retries and timeouts
Error category per call

RAG applications

Retrieval, re-ranking and generation pipelines.

The problem: Bad answers often come from retrieval, not the model: empty results, wrong documents, oversized context.
What to instrument: Record retrieval and re-ranking as spans, with result counts and context size.
What you would see: Retrieval results next to the model request they fed, plus empty-retrieval failures.
What JeraViz records: Retrieval spans and result counts
Context size sent to the model
Empty-retrieval failures

Enterprise AI workflows

Triggers, agents, human steps and systems of record.

The problem: Business workflows mix agents, human steps and systems of record, and need one auditable run history.
What to instrument: Treat the whole workflow as one execution; mark human waits and external calls as spans.
What you would see: End-to-end duration, where time was spent waiting, and outcome per workflow.
What JeraViz records: End-to-end execution record
Human-wait time as its own span
Outcome and cost per workflow

Who it is for

Built for the people who run agents.

AI startups
Debug customer-reported failures quickly and see what each workflow costs, without building internal tooling.
Platform and infrastructure teams
Standardize instrumentation across teams with OpenTelemetry-compatible ingestion and workspace-level isolation.
Enterprise AI engineering
Role-based access, retention controls and a path to audit and governance. Enterprise capabilities are planned, not shipped.

Build agents with visibility. Operate them with confidence.

Understand what your agents do, identify what needs improvement and build more reliable AI systems with JeraViz.

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JeraViz Trace is in development. Sign-up is not open yet; the early-access page explains where things stand.