PapersCONCEPT
Context Engineering Observability
Observability over what data reaches AI agents, not just prompts. For complex agent systems, you need to answer "what did the LLM know?" not just "how did we ask?"
THE LEVERAGE HIERARCHY
For complex AI applications, prompt engineering alone isn't enough. You need visibility into the entire context pipeline.
THE PROBLEM
Standard LLM observability tools (Braintrust, Langsmith, Helicone) focus on prompt engineering. They track what you asked, token counts, latency. They let you edit prompts in a UI and rerun tests.
But for complex agent systems like Food Science AI, the problem isn't the prompt. It's the context. You can't fix "missing ingredient nutrients" by tweaking prompts. You need to fix the upstream data pipeline.
STANDARD VS CONTEXT OBSERVABILITY
STANDARD LLM OBSERVABILITY
(Braintrust, Langsmith, Helicone)
- •Prompt sent to LLM
- •Response received
- •Tokens, latency, cost
- •Edit prompts in UI
Limited to: "How did we ask?"
CONTEXT ENGINEERING OBSERVABILITY
(What we actually need)
- •What DATA reached each agent?
- •Was that data accurate?
- •What was the provenance?
- •What's missing from the pipeline?
Answers: "What did the LLM know?"
WHAT IT LOOKS LIKE
Context observability means tracking the data pipeline, not just the final prompt. For the Food Science reverse engineering agent:
What ingredient data reached the agent? Did it include USDA nutrients? Brand-specific formulations? Generic substitutes?
Was the math baseline accurate? Did linear programming results make it into the prompt? Were constraints properly formatted?
What's missing? Are there data gaps the agent needs but doesn't have access to?
These questions require observability that lives in the codebase, not a third-party platform UI. You need to trace data flow, validate pipelines, and fix context issues at the source.
WHY IT MATTERS
The AI industry is focused on prompt engineering because it's easier to build tools for. But the highest leverage work happens one level up: engineering the context itself.
Context Engineering Observability is infrastructure for complex AI systems. It's the difference between "tweaking prompts and hoping" and "systematically improving what the AI knows."