OBSERVABILITY FOR AUTONOMOUS SYSTEMS

Know what every agent is doing. And why.

TraceLayer turns opaque agent runs into clear execution stories. Follow decisions, handoffs, tool calls, cost, and failures across your entire AI system.

Explore the live trace Read the quickstart

Python SDKOpenTelemetry nativeFramework agnostic

Execution graph

SIMULATED LIVE

Orchestrator

Processing trace

Planning Agent

Agent runtime

Research Agent

Agent runtime

Execution Agent

Processing trace

Memory Agent

Agent runtime

Validation Agent

Agent runtime

Privacy by design

Configurable capture controls and redaction-ready telemetry.

Fast root cause

Move from failed run to the responsible span in seconds.

Open standards

Built around portable OpenTelemetry conventions.

Why TraceLayer

Agent software does not fail like conventional software. Your observability should understand the difference.

Built for systems that reason, delegate, and act.

As AI evolves from isolated prompts into interconnected workflows, traditional logs lose the thread. TraceLayer preserves causality across model calls, agents, tools, memory, and infrastructure so teams can ship with confidence.

Platform capabilities

Everything you need to understand your agents.

From the first prompt to the final response, every span, handoff, and decision is visible.

Distributed Tracing

Capture complete execution paths across complex agent workflows.

Agent Communication

Inspect messages, tool calls, and information exchanged between agents.

Performance Analytics

Track latency, token consumption, cost, and resource utilization.

Failure Detection

Identify unsuccessful tasks, unexpected behavior, and bottlenecks.

Memory Inspection

Understand how agents retrieve, store, and exchange context.

Workflow Visualization

Explore dependencies and execution sequences through interactive graphs.

Product

One platform. Complete agent observability.

Purpose-built primitives for debugging systems that reason, delegate, and act.

01 —

TraceLayer SDK

Instrument any agent workflow with a few lines of Python. Automatic context propagation connects every call.

agent.py

from tracelayer import observe
from agents import Orchestrator

@observe(workflow="market-analysis")
def run_agent_task(prompt: str):
    agent = Orchestrator(
        agents=[researcher, analyst],
        trace_messages=True,
    )
    return agent.run(prompt)

SIMULATED DEMONSTRATION DATA

02 —

TraceLayer Console

Monitor throughput, latency, token usage, estimated API cost, and errors in one operational view.

Executions

12,842

Avg latency

842ms

Tokens

8.4M

Error rate

0.82%

SIMULATED DEMONSTRATION DATA

03 —

Agent Trace Explorer

Move from a failed task to the exact agent span, input, output, and handoff that caused it.

SIMULATED DEMONSTRATION DATA

04 —

TraceLayer API

Send custom telemetry through a simple REST API and connect your existing runtime without lock-in.

request.http

POST /v1/traces
Authorization: Bearer ••••••••

{
  "workflow": "market-analysis",
  "agent": "researcher",
  "status": "completed",
  "latency_ms": 842
}

SIMULATED DEMONSTRATION DATA

Interactive demonstration

See what your agents are really doing.

Run a simulated multi-agent workflow and watch every delegation, tool call, and validation step unfold.

Market intelligence workflow

simulation_001 · DEMO TELEMETRY

Orchestrator