AI Agent Observability & Runtime Control for Production Systems
Gain deep visibility into multi-step agent executions, tool calls, and cascading token costs—with runtime execution budgets that stop runaway loops before they impact production.
Why One-Shot LLM Tracing Fails for AI Agents
Unlike simple chat interfaces where one request yields one response, autonomous agents execute iterative reasoning loops, make dynamic tool calls, and accumulate conversational context. A single user prompt can trigger dozens of downstream actions.
Traditional LLM Logging
- ✕Captures isolated prompt/completion pairs without execution lineage.
- ✕Blind to intermediate tool inputs, return values, and retries.
- ✕Passive post-hoc logs cannot halt runaway execution in real time.
Observyze Agent Observability
- Hierarchical span trees correlating parent workflows to child tool calls.
- Exact per-turn token usage, dollar cost attribution, and latency tracking.
- Configurable execution budgets and circuit breakers that halt failing loops.
Key Dimensions of Agent Telemetry
Observyze tracks every dimension of your autonomous agent execution path from SDK telemetry to active gateway controls.
Multi-Turn Trace Trees
Inspect recursive agent calls, sub-agent delegations, and tool boundaries in an interactive timeline.
Latency Breakdown
Isolate slow vector retrievals, upstream model delays, and tool execution bottlenecks across steps.
Token & Cost Attribution
Track exact prompt, completion, and cache tokens with provider-accurate pricing for every turn.
Runtime Guardrails
Enforce pre-dispatch policy checks and client-side PII scrubbing before payload transmission.
Factual Evaluations
Score completed agent traces asynchronously for hallucination, safety, and goal completion.
Execution Budgets
Define maximum dollar limits or turn counts that stop runaway agent loops during execution.
Integrate Telemetry in 3 Lines of Code
Use the lightweight TypeScript or Python SDK to instrument your agent framework without being in the critical path of model provider requests.
import OpenAI from "openai";
import { ObservyzeClient } from "@observyze/sdk";
const observyze = new ObservyzeClient({
apiKey: process.env.OBSERVYZE_API_KEY!,
projectId: process.env.OBSERVYZE_PROJECT_ID!,
});
// Auto-wrap OpenAI client for multi-turn agent tracing
const openai = observyze.wrapOpenAI(new OpenAI());
// Execute agent loop — traces capture model & tool calls
const response = await openai.chat.completions.create({
model: "gpt-4o",
messages: [{ role: "user", content: "Run analysis" }],
tools: myAgentTools,
});Frequently Asked Questions
Technical details, integration patterns, and operational controls.
Traditional LLM tracing monitors isolated prompt/completion pairs. AI agent observability tracks stateful, multi-turn reasoning loops, sub-agent handoffs, dynamic tool invocations, and cascading spend across complex graph workflows.
Explore Related Solutions
Runtime Architecture & Tooling
AI Agent Cost Monitoring
Set hard spending limits and token quotas across multi-provider AI agents.
AI Agent Circuit Breakers
Prevent runaway cost cascades by stopping failed agent loops in production.
LangGraph Observability
Debug stateful multi-agent LangGraph workflows with node-level custom span telemetry.
Want to test without installing anything?
Explore 6 pre-configured production scenarios in our interactive Demo Mode.