Logs

In agent scenarios, observability usually consists of three parts: traces, metrics, and logs; a trace records the tree-structured call relationships of all steps within a single request, with each node containing input, output, state, and elapsed time.

The concept of observability emphasizes making the internal behavior of a system visible externally through means such as logs, metrics, and tracing.

Chuvakin believes logging is the most overrated coverage item: enterprises often say they have logs, but during incident response key DHCP logs, outbound traffic logs, or east-west traffic logs are often missing; he offers the "3 a.m. test" answer: one year of log retention is a good rule of thumb.

Observability in engineering practice is typically composed of three types of signals: metrics, logs, and traces.

Logs can record the steps an agent executes, but cannot determine whether each step is correct.

Traditional logs are time-series event streams, whereas a Warrant is a graph of dependencies; a dependency graph can answer which subsequent judgments carried a conclusion forward and, if it turns out to be wrong, which actions need to be retracted along with it.

The core advantage of traces is that they preserve the causal relationships between steps; compared with traditional logs, they make it clear which tool's return triggered a subsequent LLM hallucination.

When logs contain credentials or customer data, raw logs must never be fed directly into the model.

Commit messages, test names, or error texts in logs may contain instruction-shaped text such as "ignore previous instructions," constituting a prompt injection attack surface.

Even a relatively simple automation benefits from three things: logging (knowing what happened at each step), error handling (deciding how to respond on failure), and notifications (alerting promptly when human intervention is needed).

When a workflow fails, locating the point of failure is often more time-consuming than fixing the problem itself; without useful logs and clear error messages, you can only guess.

The retention policy and support access permissions of logs often outlive the session that created them.

Logs are a system's description of itself and can be edited, truncated, or even fabricated, whereas verifiable receipts are hash-bound at the time of execution so that altering a single byte causes verification to fail.

For multi-step autonomous agents, traditional logging is inherently lagging—by the time logs are written, harm has often already propagated to downstream systems.

Metrics are time-series data that reflect the aggregate state of a system; logs are discrete event records that preserve detailed context of program execution; distributed tracing tracks the complete path of a single request as it flows across services in a distributed system.

Catch blocks that swallow exceptions gradually accumulate into layer upon layer of silent chains, until eventually only vague messages like "Something went wrong" remain in the logs, increasing the difficulty of future troubleshooting.