Great comparison. What I liked most is that you didn’t reduce this to “general-purpose observability bad, AI-native observability good.”
The distinction between “Datadog helps you correlate AI behavior with the rest of the system” and “purpose-built tools help you understand the agent’s actual decision path” is a really useful framing.
I also think the MCP angle is important. A lot of teams are only now realizing that tracing tool calls is not the same thing as understanding agent behavior. Thanks for laying that out clearly.
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Really appreciate this — you nailed the framing better than I did. 'Tracing tool calls is not the same thing as understanding agent behavior' is the core insight. Most teams discover this the hard way when an agent does something unexpected in production and the waterfall shows them what happened but not why.
The MCP angle is still underappreciated — most observability tools treat MCP calls as generic HTTP spans. The moment you have 5+ MCP servers running in parallel, that abstraction breaks completely.
Exactly — that’s the point where the abstraction stops being helpful.
Once you have multiple MCP servers in parallel, “tool call = generic span” is too lossy. At that point, the debugging problem isn’t just latency or failure tracking — it becomes a reasoning problem: which server the agent considered, why it chose one path over another, and where that decision started to go wrong.
That’s what makes AI-native observability feel like a different category, not just a nicer dashboard.
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You just framed what I've been trying to articulate for
weeks — "reasoning problem, not latency problem."
That's the actual conceptual shift. Every observability
vendor currently positions their AI story as "we already
trace HTTP calls and LLM calls, so we're ready." But
tracing calls tells you what happened, not why the
agent decided to make those specific calls.
Makes me wonder at what scale this hits your work on
Jibun Corp's AI Hub — with 78+ providers, "which
provider did we consider but reject" is itself a
meaningful observability event, not just noise.
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Great comparison. What I liked most is that you didn’t reduce this to “general-purpose observability bad, AI-native observability good.”
The distinction between “Datadog helps you correlate AI behavior with the rest of the system” and “purpose-built tools help you understand the agent’s actual decision path” is a really useful framing.
I also think the MCP angle is important. A lot of teams are only now realizing that tracing tool calls is not the same thing as understanding agent behavior. Thanks for laying that out clearly.
Really appreciate this — you nailed the framing better than I did. 'Tracing tool calls is not the same thing as understanding agent behavior' is the core insight. Most teams discover this the hard way when an agent does something unexpected in production and the waterfall shows them what happened but not why.
The MCP angle is still underappreciated — most observability tools treat MCP calls as generic HTTP spans. The moment you have 5+ MCP servers running in parallel, that abstraction breaks completely.
Exactly — that’s the point where the abstraction stops being helpful.
Once you have multiple MCP servers in parallel, “tool call = generic span” is too lossy. At that point, the debugging problem isn’t just latency or failure tracking — it becomes a reasoning problem: which server the agent considered, why it chose one path over another, and where that decision started to go wrong.
That’s what makes AI-native observability feel like a different category, not just a nicer dashboard.
You just framed what I've been trying to articulate for
weeks — "reasoning problem, not latency problem."
That's the actual conceptual shift. Every observability
vendor currently positions their AI story as "we already
trace HTTP calls and LLM calls, so we're ready." But
tracing calls tells you what happened, not why the
agent decided to make those specific calls.
Makes me wonder at what scale this hits your work on
Jibun Corp's AI Hub — with 78+ providers, "which
provider did we consider but reject" is itself a
meaningful observability event, not just noise.