AI agents don't behave like traditional software — they loop, branch, and call tools in unpredictable ways. Here are the best platforms to trace, monitor, and evaluate them.
Traditional monitoring was built for request-response services. You send a request, you get a response, you measure latency and error rates. Done.
AI agents break that model entirely. An agent might call a tool, get a result, decide to call another tool, loop back, call an LLM again, and produce a final answer — all in a single "request." If something goes wrong, you can't just look at a 500 error. You need to trace the entire reasoning path.1
That's where observability platforms for AI agents come in. They give you tracing, evaluation, and monitoring specifically designed for non-linear, stochastic, tool-calling systems.
| Dimension | Traditional Observability | AI-Native Observability |
|---|---|---|
| Unit of work | Request/response | Trajectory (multi-step reasoning chain) |
| Debugging | Logs, stack traces | Traces with LLM calls, tool calls, agent decisions |
| Evaluation | Uptime, latency, error rate | Correctness, faithfulness, hallucination rate, cost per task |
| Key standard | OpenTelemetry (OTEL) | OTEL + LLM-specific spans + prompt/response logging |
The industry is converging on OpenTelemetry as the backbone, but AI-native platforms add layers for prompt management, LLM-as-judge evaluations, and agent step tracing.1
Portkey sits between your application and the LLM providers, acting as a gateway that captures every call. It gives you request-level tracing, prompt management, fallback routing, and spend tracking out of the box.
For agent workloads, Portkey's ability to trace multi-step tool calls and provide failover between models is invaluable. If your agent hits a rate limit or a model degrades, Portkey can route to a fallback without breaking the flow.
| Dimension | Detail |
|---|---|
| Best for | Production agent gateways with failover |
| Tracing | Full OTEL-compatible span tracing |
| Evaluation | LLM-as-judge evaluations built in |
| Pricing | Free tier + usage-based |
LiteLLM provides a single interface for calling 100+ LLM providers, which is essential when your agent needs to switch between models based on task complexity. But its real observability value is in spend tracking and usage monitoring.
Every model call gets logged with token counts, latency, and cost. For agent systems that might make dozens of LLM calls per task, LiteLLM's built-in tracking helps you understand where your budget is going and which models are performing best.2
| Dimension | Detail |
|---|---|
| Best for | Multi-model cost & usage tracking |
| Tracing | Per-call logging with token breakdowns |
| Evaluation | Basic success/failure + latency metrics |
| Pricing | Open source (self-host) + cloud tier |
Datadog is the heavyweight champion of traditional APM, and its distributed tracing capabilities extend naturally to AI agents — especially when those agents are embedded in larger microservice architectures.
Datadog's strength is end-to-end visibility: you can trace an agent call from the user's request, through the agent's reasoning loop, into the LLM provider, and back out to any downstream services the agent calls. Its dashboards and alerting are best-in-class for teams already in the Datadog ecosystem.3
| Dimension | Detail |
|---|---|
| Best for | Enterprise microservice + agent tracing |
| Tracing | Full distributed tracing with OTEL |
| Evaluation | Custom metrics + anomaly detection |
| Pricing | Per-host + per-million-spans |
The biggest risk with AI agents is invisible failure — the agent appears to succeed but hallucinates, loops infinitely, or calls the wrong tool. "Glass-box" evaluation means you can inspect every step of the agent's reasoning path, not just the final output.1
Platforms that support OTEL-based tracing let you replay agent trajectories, identify where the reasoning broke down, and set up automated evaluations that catch failures before they reach users. This is the difference between "it works" and "we know it works."
If you're building production AI agents, observability isn't optional. Start with Portkey if you need a gateway with built-in agent tracing. Use LiteLLM for multi-model cost management. And bring in Datadog when your agents are part of a larger enterprise stack that needs end-to-end distributed tracing.
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