A traditional application that fails leaves a stack trace. An agent that fails can leave nothing more than a wrong or unexpected final answer, with no visibility into the chain of reasoning, tool calls, and decisions that produced it — which makes observability a non-negotiable, not a nice-to-have, for any production agentic system.
Traditional application observability tracks deterministic code paths — the same input…
Full reasoning traces — every intermediate step an agent takes, not just the final output:…
For agents that take consequential actions, observability isn’t only about catching bugs…
Traditional application observability tracks deterministic code paths — the same input reliably produces the same execution trace. Agentic systems are non-deterministic by design: the same input can lead the agent through different reasoning paths, tool calls, and intermediate decisions on different runs. Standard logging and APM tooling, built around deterministic execution, miss most of what actually matters for understanding why an agent did what it did.
For agents that take consequential actions, observability isn’t only about catching bugs after the fact — it’s what makes a human-approval checkpoint actually meaningful. An approval step that shows a reviewer only the agent’s proposed final action, without the reasoning and evidence that led there, forces either blind trust or a separate investigation each time. Good observability surfaces the reasoning trace alongside the proposed action, so the human reviewer can actually evaluate it quickly.
Production observability data is also your best source of real evaluation test cases — traces where the agent behaved unexpectedly, took longer than expected, or a user corrected its output are exactly the examples worth feeding back into your evaluation suite and, where relevant, your red-teaming scenarios. Treating observability and evaluation as connected, not separate, programmes closes the loop between what happens in production and what gets tested before the next release.
General APM tools can capture request-level metrics (latency, errors, cost) but typically don’t natively capture reasoning traces or tool-call-level detail specific to agentic systems. A growing set of purpose-built LLM/agent observability tools fill this gap; the right choice depends on your existing stack and how deep your agent architecture goes.
Logging and tracing themselves add minimal overhead if implemented asynchronously, though storing and analyzing full traces at scale has real infrastructure cost — which is usually far smaller than the cost of debugging a production incident with no trace data at all.
Rarely in full — reasoning traces are primarily an internal debugging and trust tool. A simplified, human-readable summary of what the agent did and why is sometimes appropriate for end-user transparency, but the full technical trace is usually noise for a non-technical audience.