Obserability in the Agentic AI era
Langchain published on Feb 21:st a very insightful and structured paper about Agent observability in the Agentic AI era which is taking the industry with storm.
https://blog.langchain.com/agent-observability-powers-agent-evaluation/
Here are some short summaries of the content:
New challenges compare with traditional software debugging
From debugging code to debugging reasoning
Change of testing methodology for software when agent behavior emerges only at runtime
Major observability components like runs, traces and threads in agent calls
Growth of tracing data will be gigantic for debugging purposes
Mitigations:
Single-step evaluation
Full-turn evaluation
Multi-turn evaluation
Other evaluation concepts
Offline evaluation
Online evaluation
Ad-hoc evaluation
An example of troubleshooting workflow for Agents:
User reports incorrect behavior
Find the production trace
Extract the state at the failure point
Create a test case from that exact state
Fix and validate
On the blog page there are as well a number of case studies of using Langsmith for Agent Observability troubleshooting. As this is so new and fresh, most of the tooling vendors are yet catching up frenetically in this area.
https://blog.langchain.com/tag/case-studies/
I asked Claude to provide me with a summary of the AI agent observability field, and below are the summary table that Claude has provided based on the evaluation concepts that Langchain provided in the blogpost.
My take away:
If we think it is a challenge migrating monitoring to observability for the microservices and kubernetes containers, degree of difficulties and challenge grow hundred times in the Agentic AI due to the following changed behavior of software:
Testing and verification appears only at run-time, traditional tests are obsolete
Number of code lines to trace and debug grow to astronomical level
Non-deterministic nature of the LLM reasoning outcome
The interaction model between the AI agents
We are at the dawn of a new era with a lot of doors of opportunity open for innovation and new technology. Thanks to the fact that we have smarter AI LLM and tools now, those observability challenges with huge datasets and iterative testing cycles is just what AI is good at.