From: aidotengineer
Observability is crucial for understanding and managing complex systems, and its significance is amplified in the context of artificial intelligence (AI) systems and agents [00:08:21]. DataDog, as an observability and security platform for cloud applications, focuses on making it easier to observe what’s happening in a system and take action [00:01:22].
Why Observability Matters in the AI Era
In the current era of AI advancement, where intelligence is becoming “too cheap to meter” [00:02:24], the need for robust observability is paramount [00:08:26]. As AI agents become more sophisticated and take on critical tasks, understanding their internal workings and potential issues becomes vital.
Key reasons for the importance of observability include:
- Debugging Complex Workflows Modern AI agents, like those developed by DataDog, involve complex, multi-step calls that can number in the hundreds and include continuous looping and tool decisions [00:12:26]. Without proper observability, it’s impossible to understand what’s occurring [00:12:41].
- Situational Awareness Observability provides the necessary situational awareness to debug problems effectively and saves time [00:11:44].
- Preventing Afterthoughts Observability should be an integral part of AI system development, not an afterthought, given the inherent complexity of AI workflows [00:11:41].
Observability in Practice at DataDog
DataDog has been incorporating AI since around 2015 for features like proactive alerting, root cause analysis, impact analysis, and change tracking [00:01:46]. With the current “era shift” in AI, they are developing “AI agents” that leverage their platform [00:02:09]. This requires building out new types of observability [00:03:17].
LM Observability
DataDog has introduced a new view called “LM observability” within its product, which has proven very helpful [00:11:48]. This feature ties into DataDog’s existing full observability stack, which can monitor GPUs, LLMs, and entire systems end-to-end [00:11:57]. It’s particularly beneficial for AI systems because it can group a wide variety of interactions and calls to different models (hosted, running, or via API) into a single “pane of glass” for easier debugging [00:12:05].
Agent Graph for Workflow Visualization
To address the complexity of observing AI agents, DataDog has developed an “agent graph” view within its observability tools [00:12:46]. This graph allows users to visualize and understand the complex workflows of an agent, much like the agent itself perceives them [00:12:52]. For example, if an error occurs within a complex workflow, it can be highlighted as a bright red node on the graph, making it human-readable and significantly easier to identify and debug issues [00:13:01].
Broader Implications
The speaker emphasizes that beyond building internal agents, companies should anticipate a future where AI agents become significant users of SaaS products like DataDog [00:14:01]. This means thinking about providing context and API information optimized for agent consumption [00:14:37]. Strong observability will be critical not just for monitoring human-driven systems but also for overseeing these autonomous agent interactions.
Lessons Learned in Building AI Agents
DataDog’s experience in building AI agents has yielded several insights, including:
- Scoping Tasks for Evaluation It’s easy to build quick demos, but much harder to clearly define “jobs to be done” and scope work for evaluation [00:08:01]. Tasks should be measurable and verifiable at each step, a significant pain point for many working with agents [00:08:52].
- Importance of Evaluations Deeply considering evaluation from the start is crucial, as everything in the “fuzzy stochastic world” of AI requires good evaluation—from small initial tests to living, breathing test sets [00:09:31].
- Building the Right Team Teams should be seeded with a few ML experts but primarily consist of optimistic generalists who can move fast and embrace ambiguity [00:10:15].
- Evolving UX Traditional user experience (UX) patterns are changing, and developers should be comfortable with agents acting more like human teammates [00:11:04].