The Anatomy of Agentic Observability

[00:00:01] Welcome to Safe and Sound AI.

[00:00:03] And today we're diving deep into something huge. Mm-hmm. You know, you hear 2025 called the year of AI agents, right?

[00:00:09] Mm-hmm. Well, I'm definitely hearing that buzz.

[00:00:11] But the really big story, the one we wanna unpack today, it's actually the rise of, uh, multi-agent systems.

[00:00:18] Exactly. That's where things get really interesting and, uh, challenging. So. Our mission today really is to lay out what AI agents actually are. Maybe clarify that a bit.

[00:00:29] Okay?

[00:00:29] Then shine a light on the unique complexities that these multi-agent systems bring to the table.

[00:00:34] Right?

[00:00:35] And then talk about this new approach emerging.

[00:00:38] They're calling it Agentic Observability. It's all about building AI. We can actually rely on, and, you know, trust.

[00:00:43] Yeah, trust is a big one. We've been digging into a lot of expert discussions, some cutting edge research too. All focused on making these agents dependable, understanding how they actually think, which is, well, it's complicated.

[00:00:55] It really is.

[00:00:56] So let's just jump in. It's not just a tech upgrade. It feels like it brings a whole new layer of complexity we need to get our heads around.

[00:01:04] Absolutely. And maybe the best place to start is what is an AI agent compared to say, the LLMs, the large language models everyone's familiar with.

[00:01:12] Good point.

[00:01:13] Because agents go way beyond. LLMs are amazing at processing info, generating text images.

[00:01:18] Sure.

[00:01:19] But agents, they do things. They, uh, they plan, they make decisions on the fly. They can grab tools like use an API or search a database.

[00:01:27] Okay. So they're more active.

[00:01:28] Much more active, and they learn from what happens.

[00:01:30] Think less like a, uh, a static knowledge base and more like an autonomous operator working towards a goal.

[00:01:37] Right. Okay. And you mentioned multi-agent systems.

[00:01:39] Yeah.

[00:01:40] So if one agent is like that operator,

[00:01:42] then multi-agent systems are like moving from that single skilled worker to a whole coordinated team of specialists.

[00:01:48] That's where the real innovation, the big opportunities are now

[00:01:51] a team of AIs.

[00:01:53] Yeah. Agents designed to collaborate handoff tasks, work together on really complex workflows that one agent just couldn't handle alone.

[00:02:01] And I guess the market sees that potential.

[00:02:03] Oh, absolutely. The projections are kind of staggering.

[00:02:06] We're looking at the US Ag agent AI market going from maybe $1.6 billion now.

[00:02:11] Okay.

[00:02:11] To potentially almost $59 billion by 2034. That's huge growth.

[00:02:16] Wow. Okay.

[00:02:18] And it's not just about the money, it's the shift right. AI isn't just reacting anymore, it's becoming a proactive partner. It's about tackling problems that were just too messy, too complex before,

[00:02:27] and businesses are seeing this.

[00:02:29] Over 90% of enterprises, according to the research, see this agentic AI transforming their competitive landscape.

[00:02:35] And crucially, it's these multi-agent systems that they expect will drive the biggest jump in AI adoption.

[00:02:40] Okay. But with great power, comes great complexity, I assume.

[00:02:43] That's the catch.

[00:02:44] That's the challenge we need to talk about. The sources are highlighting that these multi-agent setups can demand, get this, up to 26 times the monitoring resources compared to just a single agent app.

[00:02:54] 26 times!

[00:02:55] Yeah.

[00:02:55] That's enormous. Why? What makes it so much harder to watch?

[00:02:59] Well think about it. Each single agent is already generating its own, uh, its reasoning trace like its thought process. Right?

[00:03:06] It's logs of what tools it used. Its decision paths. That's already a lot. But now multiply that and then add the really tricky part. How do you monitor the interactions? The coordination? How information flows between them? How one agent's decision affects the next one down the line.

[00:03:25] It sounds like untangling.

[00:03:26] Exactly. It's a whole different ball game than monitoring traditional software

[00:03:31] Which makes me think. Our usual tools, the ones we've used for years for monitoring software, they're probably not cut out for this, are they?

[00:03:37] They really aren't.

[00:03:38] Traditional application performance monitoring, APM, it was designed for a different world.

[00:03:43] How so?

[00:03:43] Well, APM tools were built for applications that behave predictably.

[00:03:47] You know, deterministic logic. They track things like response times, error rates. API calls that follow set paths. They're great for that kind of linear, predictable software.

[00:03:56] But AI agents are anything but linear and predictable

[00:03:59] Precisely. Their autonomy.

[00:04:01] Their dynamic decision making. The way they grab different tools based on the situation, it just breaks that old APM framework.

[00:04:08] They're improvising in a way.

[00:04:09] They kind of are. And then you stack multiple agents together and the complexity just skyrockets more decisions, more interactions between agents, dependencies across the whole session.

[00:04:20] It's exponential.

[00:04:22] So what can't the old tools tell us? What are the blind spots?

[00:04:25] Huge ones like the fundamental question. Why did an agent choose path A instead of path B? APM can't tell you that.

[00:04:33] Was that choice actually okay according to our company, rules or compliance? No idea from traditional tools.

[00:04:39] Did the agent learn from a mistake? Did it reflect and adapt or just blunder? Again, you can't see that self-correction loop.

[00:04:45] And the big one for multi-agent systems.

[00:04:47] Yeah. How do you find the root cause when something goes wrong across several interacting agents? Which one started the problem? Where did the handoff fail?

[00:04:55] Traditional APM just sees maybe an endpoint error, not the whole distributed mess.

[00:05:00] And that lack of visibility must worry people deploying this stuff.

[00:05:04] It's the top concern. Enterprise leaders, their biggest worries about agentic AI at scale are security, trust, compliance, and just basic oversight.

[00:05:14] Makes sense

[00:05:14] Because without knowing what these autonomous systems are really doing, you're flying blind and that can lead straight to regulatory fines, system outages, serious damage to your reputation.

[00:05:25] You don't want your AI going rogue, silently.

[00:05:29] Okay? So the old ways won't work. The risks are high. What's the answer then? This agentic observability thing?

[00:05:35] That's exactly it. It's presented as the necessary evolution, a new approach built specifically for these distributed thinking, acting, agentic applications.

[00:05:43] And you mentioned it's not just for when things are live in production.

[00:05:47] Crucially, no, it's just as vital during development. Imagine trying to build and debug one of these complex multi-agent systems without seeing how the agents are reasoning or interact.

[00:05:56] Yeah, that sounds impossible.

[00:05:57] Right? So agentic observability gives dev teams that visibility while they're building.

[00:06:03] It helps them debug the agent's logic, optimize performance, catch coordination issues before they ever hit production. It's like having x-ray vision into the AI's thought process as you create it.

[00:06:13] So how does it work? What does it combine?

[00:06:14] It takes the good stuff from traditional APM, like infrastructure metrics, basic logs.

[00:06:20] And integrates it deeply with monitoring specifically for models and LLMs. So you get insights into model predictions, data quality issues, and importantly, the explainability behind decisions.

[00:06:32] The why.

[00:06:33] Exactly. It aims for that truly comprehensive view of these complex distributed systems.

[00:06:39] You also mentioned hierarchical visibility. What does that mean in practice?

[00:06:42] Think of layers. A multi-agent app generates data at multiple levels. You've got the overall application performance, individual user sessions, then the actions of each specific agent within that session.

[00:06:52] Okay drilling down.

[00:06:53] And then even deeper the specific tools that agent called the API requests it made. Agentic obervability gives you a view that lets you navigate these layers. You can start high level, see an issue in a session.

[00:07:05] And then drill down through the specific agent trace its entire journey. That's the trace. And look at individuals steps or actions. Those are the spans.

[00:07:14] We can follow the whole chain of events.

[00:07:16] Precisely.

[00:07:16] You get the full audit trail, complete visibility into how agents are interacting, where dependencies lie, where things went wrong across that chain.

[00:07:24] Okay. That makes sense. It's about seeing the connections.

[00:07:26] It is, and that brings us nicely to sort of the anatomy of an agent that we can observe because agentic observability, it's not just about tracking server load or model accuracy and isolation.

[00:07:38] It's really about understanding the agent's entire cognitive and operational loop, how it perceives, thinks, acts, and learns.

[00:07:46]  The whole lifecycle.

[00:07:46] The whole lifecycle. And observing that loop lets teams monitor, yes, but also control and protect the agent's performance and its behavior. Keep it on track.

[00:07:55] Okay, so let's walk through that loop. You mentioned five stages, this agent lifecycle.

[00:08:00] Yeah, it's a useful model. Think of it as a continuous feedback cycle. Stage one is thought, ingest, retrieve, interpret.

[00:08:09] Okay, thought. What happens here?

[00:08:11] This is the agent taking in the initial request or prompt. It pulls relevant info from its memory, maybe past interactions or stored data.

[00:08:19] It forms what's called a belief state. Basically, it's current understanding of the situation, the task, the context.

[00:08:25] It's mental model?

[00:08:26] Sort of, yeah. Then it interprets the goal it's been given and formulates a plan to achieve it. And observability here captures things like the input prompt, how well it retrieved memory, did it understand the goal correctly?

[00:08:38] What was the plan it generated?

[00:08:40] So you see its intent before it even acts.

[00:08:42] Exactly. You see if it's starting off on the right foot, if it understood the assignment crucial first step.

[00:08:47] What's next?

[00:08:47] Stage two is action. Plan and select tools. So based on that plan, it just formulated. The agent now decides which specific tools or APIs it needs to use.

[00:08:58] And observing this stage shows you those tool choices, the reasoning path it took, why it picked that tool, and the sequence it plans to execute them in. It's about making sure it's choosing the right methods.

[00:09:10] Got it. Thought then action planning.

[00:09:12] Stage three must be execution.

[00:09:14] You got it? Execution, perform tasks and capture outputs. This is where the agent actually does the thing. It invokes the tools, calls the APIs it selected.

[00:09:23] The rubber meets the road

[00:09:24] Pretty much. And the Observability here is vital for runtime diagnostics. It captures the inputs and outputs of those tool calls.

[00:09:31] Any errors that pop up, latency, how long did it take? Was the tool effective? Did it return the right info? And importantly, signals about success or failure of that specific action. Did the API call work or fail?

[00:09:43] So this is where you spot immediate problems?

[00:09:45] Yeah.

[00:09:45] Like a broken API connection.

[00:09:47] Exactly.

[00:09:47] Runtime issues live here. Then comes a really interesting stage. Number four, reflection. Evaluate success, failure and adapt.

[00:09:55] Reflection the agent reflects.

[00:09:57] Yeah, this is a key capability for more advanced agents. It basically self critiques its own performance. It looks back at its actions, compares them to the original goals and plan.

[00:10:07] How does it do that?

[00:10:08] Could involve things like trajectory scoring. Essentially grading itself on how well it followed the plan and achieved the goal. Was it efficient? Did it get stuck? It analyzes errors and crucially, it uses this reflection to adapt its future behavior. It learns from mistakes.

[00:10:24] This reflection can also be triggered externally, maybe by a human flagging an issue or a separate trust model stepping in.

[00:10:31] So it's a learning loop built right in.

[00:10:33] Ideally, yes. It's designed for self-improvement. Which leads to the final stage, number five, alignment.

[00:10:39] Alignment. This sounds like guardrails.

[00:10:41] That's exactly what it is. This is where safety nets, policy enforcement, and fallback logic kick in.

[00:10:46] If the agent starts to do something off policy outside, its allowed rules or boundaries. This is the stage where trust models or even human in the loop systems can intervene.

[00:10:56] They can pause the agent, reroute the task, prevent unsafe or non-compliant actions. It ensures the agent stays within acceptable operational and ethical limits.

[00:11:09] Okay, so thought, action planning, execution, reflection, and alignment. That covers the whole process,

[00:11:15] Right. And together they form this closed feedback loop.

[00:11:19] Observing every stage gives you the full picture, not just what happened, but why decisions were made, where coordination failed and how to improve the whole system over time. It's about understanding the agent's inner workings.

[00:11:32] That makes a lot more sense. Can we make it even more concrete?

[00:11:35] Sure. Let's do that. Imagine you're using a travel app to book a trip, say New York to Paris. You need flights, a hotel, maybe a rental car.

[00:11:42] Okay, classic travel, booking

[00:11:43] behind the scenes. This app uses multiple AI agents. There's a flight agent, a hotel agent, a car rental agent. They need to work together to fulfill your requests.

[00:11:51] Right? Coordinating the dates, locations.

[00:11:53] Mm-hmm.

[00:11:53] Exactly. Now, let's say something goes wrong. Maybe the hotel isn't available for the flight dates or the car rental place has an issue. With traditional monitoring, what do you see?

[00:12:03] Probably just an error message. Booking failed.

[00:12:07] Probably maybe a vague API error.

[00:12:10] Log somewhere deep in the system. It tells you nothing about which agent failed or why the coordination broke down.

[00:12:15] You're totally in the dark

[00:12:17] Completely.

[00:12:18] Was it the flight agent sending bad dates to the hotel agent? Did the hotel agent just time out? Did the car rental agent get the wrong location passed to it?

[00:12:25] An error in one can easily mess up the others downstream.

[00:12:29] A cascading failure?

[00:12:30] Yes, and it's a nightmare to debug with old tools. But now. Picture it with a agentic Observability,

[00:12:36] You get that hierarchical view we talked about. You start at the user session, your booking attempt, you see an error.

[00:12:41] You can then drill down

[00:12:43] Into the individual agents.

[00:12:44] Exactly. You can follow the trace, see the request, go to the flight agent. Okay, that worked. See it hand off dates to the hotel agent. Ah, there's the problem. Maybe the hotel agent misinterpreted the dates or its API call failed.

[00:12:57] And you can pinpoint it.

[00:12:58] You can pinpoint it precisely. You see the specific interaction, the specific span where the error occurred, the inputs and outputs at that point. Was it bad data handoff, a faulty decision by the agent, a policy violation? You see the conversation between the agents.

[00:13:14] That sounds incredibly powerful for debugging.

[00:13:16] It is, the benefits are clear, much faster debugging, which means better user experiences because problems get fixed quickly,

[00:13:23] Right.

[00:13:24] And fundamentally reduced operational risk. You can catch and fix these complex interaction issues proactively before they cause major headaches. It builds confidence to actually deploy these powerful systems.

[00:13:36] So it really feels like agentic observability is aiming to be more than just a fancy dashboard. You called it a control layer.

[00:13:43] Yeah. That's how it's being framed. It's not just passive monitoring. It's seen as this convergence point where observability, seeing what's happening meets operational trust and strategic alignment.

[00:13:52] The goal is to use these deep insights to build AI systems that aren't just capable, but also transparent, accountable, and genuinely ready for serious production use cases.

[00:14:02] And looking forward, what are the guiding ideas shaping this field?

[00:14:07] There are a few key principles driving its future. One is treating reflection as a first class signal. Don't just log actions. Actively capture the agent's own self critiques, its reasoning about its success or failure. Get that why.

[00:14:21] Understand is self-assessment.

[00:14:23] Exactly. Another is runtime semantic tracing. This means going beyond just logging API calls or CPU usage. It's about tracing the agent's plan.

[00:14:32] Its evolving. Belief state, the whole chain of tools. It's using all in real time, deeper insight into its reasoning process as it unfolds.

[00:14:41] Okay, more than just surface data?

[00:14:43] Much more, then there's behavior centric, debugging. Instead of just looking for code bugs, actively look for undesirable behavior.

[00:14:49] Like going off script.

[00:14:50] Yeah. Detecting when an agent deviates on policy, when coordination between agents fails, when it simply misses the goal it was given. Recognizing that many failures in these systems are misalignments. Not just bugs.

[00:15:03] It's a subtle but important distinction.

[00:15:05] It is. And finally, integrating guardrails and trust models directly into the observability framework.

[00:15:11] Having the ability based on observed behavior to escalate issues, reroute tasks or trigger recovery mechanisms live in production. When an agent starts to stray, it's about building in that safety net dynamically.

[00:15:23] So tying it all together. As AI moves from just making predictions to being these active dynamic agents

[00:15:30] Observability has to evolve too.

[00:15:32] It can't just be about looking at logs after the fact. It needs to be about real-time understanding of complex behavior, especially with these teams of agents working together.

[00:15:40] It really feels like the key to unlocking what these multi-agent systems can truly do.

[00:15:44] It really does. Without this level of insight and control, the complexity could just be overwhelming.

[00:15:49] Which leads to a final thought for you, our listener. Given this kind of deep visibility, this potential for control, that agentic observability promises, were really complex, really messy problems out there, problems that maybe seemed impossible to manage or trust with AI before, could you now imagine tackling with these sophisticated multi-agent systems?

[00:16:09] It certainly opens up some fascinating possibilities.

[00:16:11] This podcast is brought to you by Fiddler AI. For more on observability , or more details on the concepts we discussed, see the article in the description.

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