Why This Comparison Matters Now
In 2026, according to McKinsey's State of AI 2024 report, 72% of organizations now use AI in at least one business function, up from 50% in previous years. That adoption curve sounds like progress. What it actually means is that a large share of those organizations have AI systems making real decisions, approving loans, flagging medical records, drafting contracts, with no reliable way to reconstruct why a specific decision was made on a specific day.
The question of how to capture that reasoning has split the builder community into two camps. One group logs what they can, manually, using whatever observability tooling they already have. The other is moving toward purpose-built frameworks like AgentLedger, an open-source Python SDK designed specifically for AI agent decision tracing. Both approaches work in narrow conditions. Neither is universally correct. This article lays out where each one holds up and where it breaks.
Approach A: Manual Logging with General-Purpose Observability Tools
Most teams start here. They already have a logging stack, whether that's Datadog, Grafana, or a simple structured JSON logger writing to S3. Adding AI agent events to that stack feels natural. You instrument the reasoning node, capture inputs and outputs, and pipe everything into your existing dashboards.
