Why your AI agent fails in production & how tracing helps

Your agent works perfectly in staging. It answers questions, calls the right tools, and completes tasks in a reasonable number of steps. Then it hits production. A user asks a follow-up question on turn 11 of a conversation, the agent calls the wrong tool, and the response is completely off. Logs show a clean 200 for every request. No errors, no timeouts, nothing to investigate. Except the user got a bad answer, and nobody can tell you why.

This is the core problem generative AI (GenAI) agent tracing exists to help address. Unlike traditional monitoring, where you're looking at HTTP status codes and response times, agent tracing captures the decision path that led to an outcome. It tells you not just what happened, but which tool calls, memory updates, and intermediate steps shaped the result. This article covers what agent tracing is, why traditional observability tools struggle with agentic workloads, the GenAI spec plus an OTel primer you'll use in practice, and which metrics make tracing actionable in production.

What GenAI agent tracing really is

GenAI agent tracing is an observability approach for multi-step AI workflows where the execution path isn't defined in your code. It emerges at runtime from LLM decisions. Tracing captures the reasoning chain, tool calls, and memory operations across workflows that may span dozens of steps.

Three concepts make this concrete: a run is one unit of work (a model call or tool invocation), a trace is the complete execution tree for a single request, and a thread is a sequence of traces across a multi-turn conversation. That last one, the thread, is what separates agent tracing from plain LLM monitoring. If an agent works fine for 10 turns and fails on turn 11, looking at turn 11 alone may not help. The full thread might show that the agent stored a bad assumption in memory on turn 6, and every turn after that built on it.

Agent tracing also works across layers. At the LLM level, you capture prompts, completions, token usage, and latency. At the orchestration level, you track prompt chains, retries, and tool timing. At the agentic level, you add multi-step reasoning paths, memory references, and the intermediate outputs that shaped the final response. That agentic layer is where compounded failures usually become visible.

Why agentic systems break traditional observability

Once you know what tracing captures, the next question is why standard observability stacks fall short. Traditional Application Performance Monitoring (APM) tools can show healthy infrastructure while the agent quietly burns through tokens doing useless work.

Non-deterministic execution paths

Traditional APM assumes relatively predictable call graphs, but agents break that assumption. The same request can succeed or fail on different runs without any code changes. Agents loop, branch, revisit steps, and sometimes spawn sub-agents. In practice, tracing LLM calls and tool calls is often required for debugging, auditability, and regression testing.

The dual-failure problem

Agentic systems inherit normal infrastructure failures, like HTTP timeouts, protocol errors, and bad credentials, and add stochastic cognitive failures on top, including incorrect tool use, faulty planning, or hallucinated outputs. Traditional APM usually sees the first category, but often misses the second.

Infrastructure metrics lie about agent health

An agent stuck in an unproductive loop can still generate valid HTTP 200 responses. When agents are given open-ended goals, they may keep running without knowing whether the task is complete or whether they've stopped making progress, so infrastructure looks healthy while the agent burns budget.

Multi-agent handoff failures

In multi-agent systems, failures often show up at handoffs. One agent passes partial context, stale memory, or an ambiguous intermediate result to the next. Infrastructure traces capture latency and status codes, but they don't show whether the receiving agent got the right state or inherited the wrong constraints.

Silent policy violations

Standard tooling also misses silent violations where constraints are implied but not explicitly enforced. An agent can break company policy or compliance rules without producing any system error. You may see no exception and no alert, only a bad decision chain you never recorded.

The four signal types for agentic systems

Those gaps are why agent teams need more than classic app monitoring. The OpenTelemetry (OTel) GenAI special interest group is standardizing how to observe these systems, and in production, teams typically work across four signal types:

• Traces and spans: Follow each model interaction's lifecycle and capture decision paths through agent graphs. The OTel GenAI and agent spans specs define standard attributes for model calls and multi-step agent workflows.
• Metrics: Aggregate high-level indicators like request volume, latency, and token counts across providers.
• Logs: Capture discrete system events and still matter for infrastructure debugging, incident timelines, and correlation with the other signals.
• Events: Record GenAI-specific interactions like user prompts and model responses, giving you a granular view of what the model actually received and returned.

One thing to keep in mind: the OTel specs recommend against capturing large or sensitive content by default, with an opt-in path for prompt and completion logging. As of early 2026, the OTel GenAI semantic conventions are still experimental and may change, so teams should expect possible schema changes rather than assuming every downstream dashboard will stay fixed.

What to measure: metrics that make agent tracing actionable

Once you know which signals to collect, the practical question becomes what to measure. A useful way to organize production observability for agentic systems is across four dimensions: task success, latency, cost, and reliability.

Task success & quality

Start with the obvious question: did the agent actually do the job? Task completion rate, tool call success rate, and human override rate are useful baseline metrics. If your agent says it completed a task but users keep correcting it, your uptime graph isn't telling the real story.

It's also worth tracking tool behavior directly: which tools fail most often, which ones get retried repeatedly, and which ones show a mismatch between successful API execution and useful output. That helps separate infrastructure issues from planning issues.

Latency & normalization

Static latency thresholds can be misleading for agentic workloads. LLM latency depends heavily on output length. Ten seconds for 2,000 tokens may be acceptable, but ten seconds for 50 tokens probably isn't. What matters is latency relative to output size, and distributions rather than averages, because averages hide the weird runs that hurt user experience.

Cost

Token consumption per trace, aggregated by session, feature, and user, is one of the more useful inputs for cost attribution. Cost per successful task often matters more than cost per request. An agent that completes a workflow in three steps may be cheaper than one that loops through 18 tool calls and still asks the user to try again. When an agent reads memory, updates shared state, or looks up retrieved context on a hot path, the latency of that data layer contributes to overall trace duration and session cost. Semantic caching can help here too. Tools like Redis LangCache recognize when queries mean the same thing despite different wording, so repeated intents get served from cache instead of burning another LLM call.

Reliability & control

Reliability for agents isn't just uptime. It's whether the agent stays inside expected behavioral bounds. Useful measures include step count per task, retry rate, loop frequency, fallback rate, timeout rate, and policy-violation rate.

Watch for runaway traces in particular. A sudden increase in average step count or repeated tool retries can show up before users file tickets. If an agent starts taking far more actions to finish the same class of task, that's a reliability regression even if every API call technically succeeds.

Memory & retrieval quality

Because many agents depend on memory and retrieval, measure those layers directly too. Useful signals here include retrieval hit rate, memory read frequency, stale-memory incidents, and whether retrieved context actually appears in the final answer or plan.

A retrieval step can succeed mechanically and still return irrelevant context. The same goes for memory writes: a write can complete successfully and still make later turns worse if the stored summary is incomplete, outdated, or wrong. This is where a structured memory layer can help. The Redis Agent Memory Server separates short-term memory (in-memory data structures for instant access) from long-term memory (vector search for semantic retrieval across conversations). When those tiers are distinct, your tracing can pinpoint whether a failure originated in the short-term context window or in a stale long-term retrieval.

How tracing becomes useful in production

A user reports that an agent booked the wrong meeting. The final turn looks fine: the scheduler tool got called with a plausible time. But the trace shows the earlier retrieval and planning steps that set up that mistake. Without a connected execution tree linking model calls, tool invocations, and memory writes, you're still guessing.

That's the on-call value of tracing: you can inspect a failed task from multiple angles and see whether the model misunderstood the request, retrieval injected bad context, or a memory write earlier in the thread poisoned later turns. It shortens the distance between symptom and root cause. It also makes evaluation more realistic. Instead of grading only final answers, you can compare execution paths over time and see whether a prompt change, model swap, or new memory strategy actually improved things or just shifted the cost.

For teams building long-running or multi-agent systems, a fast state layer matters here too. Redis can support that layer with core data structures and streams for conversation state, plus the Redis Query Engine for vector search and semantic retrieval across agent memory.

Agent tracing is really about debugging decisions

You're no longer observing a fixed request path. You're observing a system that chooses what to do next, sometimes well and sometimes badly. That's why status codes and latency charts aren't enough on their own.

Good tracing makes agent behavior inspectable. It gives you visibility into decisions, memory, tools, retries, and thread-level context, so you can see why an outcome happened instead of only noticing that it did. And when you connect traces to metrics around success, latency, cost, and reliability, observability becomes something you can act on.

Redis fits this picture because agent systems depend on fast state, and Redis is built around in-memory data structures for low-latency access. It provides a complete agent memory stack: short-term memory (cache), long-term memory (vector search via the Redis Query Engine), operational state (native data structures), and real-time coordination (streams and pub/sub). That's why Redis is the most-used tool for AI agent data storage, at 43% adoption among agent developers in the 2025 Stack Overflow survey.

Instead of stitching together separate systems for caching, vector storage, and event-driven coordination, your agent workflow and your tracing can point to a single low-latency data layer. Redis also integrates with 30+ agent frameworks including LangChain, LangGraph, and LlamaIndex, so it fits into the stack you're already using. Try Redis free to see how it works with your agent architecture.