How Observability can Make or Break an Agentic AI Platform
Observability-led view of agentic AI platforms: intent-level traces, cost attribution, and why visibility shapes unit economics and trust.
Cutting through the noise
If you spend even a short amount of time on LinkedIn or YouTube today, it’s hard to miss the pattern. Feeds are flooded with posts about AI and Agentic AI. Every second demo claims autonomy. Every third video promises that agents will reshape digital products overnight. What’s far less visible are the operational realities of running an agentic AI platform. Those realities don’t usually surface in social posts or polished demos. They surface quietly, when platform owners start questioning unit economics, when backend engineers struggle to explain latency spikes, when DevOps teams chase unpredictable cost curves, and when data scientists realise their models behave very differently once stitched into long-running agent workflows. Over the last few weeks, while being directly involved in the development and assessment of an agentic AI platform, I ran into a set of recurring gaps. None of them were about model quality or agent frameworks. They were about observability or the lack of it. It felt early to talk about it, but also necessary. Because these gaps show up long before scale and they shape whether a platform grows with confidence or with fear.
What this article explores...
Agentic AI platforms often look impressive in demos, but their real challenges emerge only in production, when real users, real traffic, and real costs come into play. In many cases, the technology works as intended, yet teams struggle to explain inconsistent behaviour, rising costs, or unexpected performance shifts.
This article examines a recurring pattern in agentic systems: complexity grows faster than visibility.
The larger message of this article: agentic AI does not fail because it is complex, it fails when complexity cannot be observed, explained and governed.
A familiar platform, seen differently (Agentic)
To ground this discussion, let’s use something most of us understand instinctively: an e‑commerce platform. At a surface level, it’s simple. Users browse a catalogue, search products, add items to a cart, pay, and track delivery. Underneath, the platform manages catalogue lifecycle, inventory, pricing, promotions, payments, and fulfilment. Now imagine this platform is agentic. Instead of static workflows, agents are responsible for interpreting intent, resolving constraints, coordinating decisions, and recovering from partial failures. A user request like: “Find me a laptop under this budget, deliverable tomorrow, with a good warranty” is no longer a single path. It becomes a conversation between agents and search, pricing, inventory, delivery promise, cart validation, and payment orchestration. This is where observability stops being an infrastructure concern and becomes a core product capability.
Where things start to feel uncomfortable
In the early stages, platforms often look healthy through the lens of standard monitoring. Average request latency sits within acceptable thresholds. Token consumption per request appears stable. Infrastructure dashboards show no obvious stress - CPU, memory, and error rates remain well within limits. At this level, nothing looks broken. The discomfort begins once real user traffic starts flowing through multi-agent journeys. Dashboards still report green, but engineers begin noticing inconsistencies buried in the traces. A product search request that typically completes in 700 – 900 ms suddenly stretches beyond 3 seconds for certain queries. Token usage, previously averaging 1,200 – 1,500 tokens per request, occasionally spikes to 6,000 or more, without any obvious correlation to input size. Checkout flows that follow the same user path show a 3 – 4× difference in cost per completion, even when the outcome is identical. What initially appears as a single API request is, in reality, a chain of agent executions. This becomes visible only when tracing is examined at intent level. A simplified trace often looks something like this:
trace_id = 9f3c…
intent = search_and_checkout
[Gateway] latency=12ms
└─ [IntentResolver] latency=180ms tokens=320
└─ [SearchAgent] latency=620ms tokens=980
└─ [RefineQuery] latency=410ms tokens=1,120 retry=1
└─ [PricingAgent] latency=90ms deterministic=true
└─ [InventoryAgent] latency=140ms deterministic=true
└─ [CartAgent] latency=310ms tokens=2,450 retry=2
└─ [PaymentAgent] latency=480ms retry=1
From the outside, this request completes successfully. From the inside, however, the picture is different. A refinement step inside the search agent triggers a retry. The cart agent enters a reasoning loop to resolve constraints. A minor retry in the payment agent adds latency without triggering any errors. None of these behaviours are visible in top-level metrics. Average latency still looks acceptable. Error rates remain low. Cost dashboards show gradual increases rather than sharp spikes. These are not edge cases. They are expected patterns in agentic systems. At this stage, many teams realise an uncomfortable truth: they can clearly observe what happened, response times, token counts, success rates but they cannot reliably explain why it happened. The observability exists at the surface, but the causal chain across agents remains largely invisible.
Why agentic systems demand deeper visibility
Agentic platforms are distributed systems with uncertainty built in. A single user intent may fan out into multiple agents, each performing a mix of deterministic logic and probabilistic reasoning. Some steps are cheap and predictable, filtering inventory, validating rules, applying promotions. Others involve LLM calls that consume tokens, introduce latency variance, and occasionally retry or loop. When all of this is measured as one opaque request, optimisation becomes guesswork. Teams may reduce model size, tweak prompts, or cache responses, without actually knowing which agent or step is responsible for cost or delay. Observability, in this context, is about separating the visible from the invisible.
What weak observability looks like on the ground
In real projects, insufficient instrumentation rarely causes dramatic failures. Instead, it creates slow erosion. Engineers start spending time debugging behaviour they can’t reliably reproduce. Product teams struggle to explain why certain journeys feel fragile. Finance teams see costs rising faster than usage growth. Users, meanwhile, experience something harder to articulate: inconsistency. Sometimes the platform feels intelligent and proactive. At other times, it feels hesitant, repetitive, or oddly conservative. All of this traces back to the same root cause, decisions being made on partial data.
The questions strong observability enables
When observability is designed intentionally, the conversations change. Teams can start answering questions such as:
- Which agent contributed most to end‑to‑end latency for this checkout?
- Which step consumed the highest number of tokens?
- Where did retries occur, and were they model‑driven or logic‑driven?
- Which user intents are disproportionately expensive compared to their business value? Each of these answers directly influences architectural and product decisions. Without them, teams are optimising in the dark.
Metrics that actually shape behaviour
Traditional infrastructure metrics still matter in agentic systems - CPU utilisation, memory pressure, network latency, and error rates remain necessary signals. But on their own, they describe system health, not system behaviour. What starts to matter far more is intent-level visibility. Instead of asking how long a request took, teams begin asking how long a user intent took to resolve. Instead of looking at aggregate token usage, they examine how tokens accumulate across agents and steps. This shift fundamentally changes how optimisation decisions are made. In practice, teams start tracking metrics such as:
- End-to-end intent latency (P50 / P95 / P99)
- Agent hop count per intent
- Token consumption per agent and per step
- Retry depth and reasoning loops
- Cost per successful intent, not per request
When these metrics are applied to an agentic e-commerce platform, patterns emerge that are invisible in standard dashboards. A search flow that looks fast at the API level may hide expensive refinement loops. Checkout journeys that succeed reliably may still vary significantly in cost depending on constraints like delivery timelines, promotions, or inventory availability. This becomes clearer when teams compare intents side-by-side.
Cost-per-intent comparison (sample)
| Intent Type | Avg Latency (P95) | Agent Hops | Avg Tokens | Avg Cost | Observed Behaviour |
|---|---|---|---|---|---|
| Product Browse | 820 ms | 3 | 640 | $0.012 | Mostly deterministic, minimal reasoning |
| Search + Filter | 1.9 s | 5 | 2,100 | $0.038 | Refinement loops dominate token usage |
| Search + Checkout | 3.8 s | 7 | 6,420 | $0.094 | Cart and constraint resolution drive cost |
| Promotion-Heavy Checkout | 4.6 s | 8 | 7,850 | $0.121 | Conflicting rules trigger retries |
| Express Delivery Checkout | 5.1 s | 8 | 6,980 | $0.108 | Delivery promise validation adds latency |
What is striking here is that none of these intents are failing. From a user perspective, the platform works. From a system perspective, however, the cost and latency profiles differ dramatically. A closer look at one of these journeys might reveal something like this:
intent_id=chk_18422
intent_type=search_and_checkout
end_to_end_latency_p95=3.8s
agent_hops=7
total_tokens=6,420
total_cost=$0.094
agent_breakdown:
- SearchAgent tokens=2,180 retries=1
- RefineQuery tokens=1,460 retries=2
- CartAgent tokens=2,460 retries=2
- DeliveryPromise tokens=320 retries=1
Suddenly, optimisation discussions become grounded. The problem is no longer “LLMs are expensive” or “latency is unpredictable.” The problem is where cost accumulates and why certain agents behave the way they do under specific constraints. Once this level of visibility exists, teams stop reacting defensively. Instead of broadly reducing model size or capping tokens, they can make deliberate decisions, simplifying reasoning paths, limiting retries, caching outcomes, or redesigning agent boundaries. The result is not just lower cost, but calmer engineering conversations. When behaviour is visible, decisions become intentional rather than reactive.
Building observability into development, not around it
A common concern raised during agentic platform development is that deep observability will slow teams down or introduce runtime overhead. In practice, this concern usually comes from treating observability as something bolted on after the system exists. When observability is designed with the agents, the cost is marginal and the payoff is immediate. The shift starts with a simple but powerful idea: treat user intent as a first-class execution unit. At the edge of the system, typically an API gateway or request handler, each user request generates a root intent identifier. This identifier is not just a correlation ID for logs; it represents a complete business journey. That intent context is then propagated naturally across agents, services, and tools. From that point on, observability becomes an extension of agent execution rather than a separate concern. Each agent emits a small, consistent set of telemetry signals:
- When it starts processing
- When it makes a decision
- When it hands off to another agent or tool
Crucially, deterministic and LLM-driven steps are treated differently. Deterministic steps are labelled as such. LLM-driven steps capture token usage, model metadata, and retry behaviour. This distinction is what later enables cost attribution and behavioural analysis.
A simplified example (Python + OpenTelemetry)
Below is a stripped-down example of what this looks like inside an agent, using OpenTelemetry instrumentation and exporting traces to AWS CloudWatch via OTLP.
from opentelemetry import trace
from opentelemetry.trace import SpanKind
from opentelemetry.context import attach, detach
tracer = trace.get_tracer(__name__)
def handle_search_agent(intent_id: str, query: str, context: dict):
with tracer.start_as_current_span(
name="SearchAgent.execute",
kind=SpanKind.INTERNAL,
attributes={
"intent.id": intent_id,
"agent.name": "SearchAgent",
"agent.type": "llm",
"query.length": len(query)
}
) as span:
try:
response, token_usage = call_llm(query, context)
span.set_attribute("llm.model", "gpt-4o-mini")
span.set_attribute("llm.tokens.prompt", token_usage.prompt_tokens)
span.set_attribute("llm.tokens.completion", token_usage.completion_tokens)
span.set_attribute("llm.tokens.total", token_usage.total_tokens)
span.set_attribute("retry.count", token_usage.retries)
return response
except Exception as e:
span.record_exception(e)
span.set_status(trace.Status(trace.StatusCode.ERROR))
raise
This is not heavy instrumentation. It is a few attributes attached at the moment decisions are made, exactly where context already exists.
How this shows up in practice
When exported via OpenTelemetry to AWS CloudWatch or a compatible backend, these spans naturally assemble into an intent-level trace:
- API Gateway span (intent entry)
- Intent Resolver span
- Search Agent span
- Refinement sub-span (if triggered)
- Cart Agent span
- Payment Agent span
Each span carries just enough metadata to answer meaningful questions later:
- Which agent ran?
- Was it deterministic or LLM-driven?
- How many tokens were consumed?
- Did retries occur?
- How much latency did this step contribute?
At no point does this require complex frameworks or verbose logging. The key is consistency - the same attributes, emitted the same way, across all agents.
Why this doesn’t slow teams down
The reason this approach scales well is that it aligns with how agentic systems are already written. Agents already know when they start, when they decide, and when they hand off. Instrumentation simply makes those moments visible. Teams that adopt this early tend to notice a shift in behaviour:
- Debugging sessions become trace-driven instead of log-driven
- Cost discussions become agent-specific instead of abstract
- Optimisation efforts focus on behaviour, not guesswork
The overhead is minimal. The discipline pays for itself. Observability, in this model, is not a monitoring layer wrapped around agents. It is part of how agents explain themselves, to engineers, to the business, and eventually, to the platform itself.
Tooling that supports this approach
Modern cloud platforms already provide most of the building blocks. On AWS, services like OpenTelemetry, X‑Ray, and CloudWatch make it possible to trace requests across services. Azure offers similar capabilities through Application Insights and Azure Monitor. Independent platforms such as Datadog, Grafana Tempo, Honeycomb, and LLM‑focused tools like Phoenix or Langfuse add higher‑level visibility into traces and token behaviour. The key is not the tool itself, but the decision to instrument around intent, agents, and outcomes.
The business consequence people underestimate
As agentic platforms move closer to autonomy, observability quietly becomes the control mechanism of the business, not just a technical capability. Without strong observability, platforms don’t usually fail fast. Instead, they drift. Costs rise without clear attribution. Product decisions become cautious. Confidence erodes first internally, then externally. With observability in place, the same platform tells a very different story.
What leaders actually start asking
Once agentic behaviour touches revenue-critical flows like checkout, promotions, and delivery commitments, leadership questions change:
- Why is cost per order rising faster than order volume?
- Which user journeys are profitable, and which are not?
- Are we scaling intelligence or just scaling cost?
- Which experiments are safe to run, and which are risky? These are business questions, but they can only be answered with intent-level data.
Connecting metrics to business outcomes
In an agentic e-commerce platform, each user intent represents a potential business outcome: browsing, conversion, upsell, or abandonment. Observability allows those intents to be measured not just by success rate, but by economic efficiency.
A simplified intent-level view might look like this:
Intent economics snapshot (monthly average)
| Intent Type | Success Rate | Avg Cost per Intent | Revenue Impact | Business Interpretation |
|---|---|---|---|---|
| Product Browse | 99.6% | $0.012 | Low | Cheap, safe to scale |
| Search + Filter | 97.8% | $0.038 | Medium | Optimise refinement loops |
| Search + Checkout | 96.4% | $0.094 | High | Core revenue driver |
| Promotion-Heavy Checkout | 92.1% | $0.121 | Medium-High | Margin erosion risk |
| Express Delivery Checkout | 91.3% | $0.108 | High | Latency impacts conversion |
Without observability, these differences are invisible. All successful orders look the same on a revenue report. With observability, leadership can immediately see where intelligence is adding value and where it is quietly eroding margin.
How lack of observability translates into business risk
When intent-level cost and behaviour are not visible, organisations tend to make defensive decisions. Typical outcomes without observability:
| Signal Missing | Resulting Business Behaviour |
|---|---|
| Cost per intent | Blanket cost-cutting (smaller models, fewer features) |
| Retry depth | Over-conservative throttling |
| Agent-level latency | Fear of adding new agents |
| Confidence metrics | Slow experimentation cycles |
The irony is that these decisions often reduce differentiation, not cost.
What changes when observability exists
When observability is designed into the platform, the conversation becomes specific and calm. Consider this comparison before and after intent-level observability:
Before vs After observability
| Dimension | Before | After |
|---|---|---|
| Cost understanding | AI costs are rising | Cart reasoning adds 42% of checkout cost |
| Experimentation | Risky, slow | Targeted, confident |
| Pricing strategy | Static | Intent-aware |
| Product roadmap | Feature-driven | Outcome-driven |
| Trust | Fragile | Measurable |
For example, a leadership team may decide that promotion-heavy checkouts are acceptable only above a certain basket value, or that express delivery reasoning should cap retries after one attempt. These are business rules, but they depend entirely on technical observability.
From monitoring to decision support
This is why observability in agentic platforms is not about monitoring for failures. Failures are obvious. What observability really enables is:
- Understanding unit economics per intent
- Measuring confidence vs cost trade-offs
- Deciding where autonomy makes sense
- Scaling intelligence deliberately, not blindly
For CEOs, this means predictability, for Product Managers, it means clarity and for Analysts, it means explainable numbers not approximations.
Agentic platforms don’t fail because autonomy is risky. They fail when autonomy is unmeasured. Observability turns agentic AI from a cost centre into a controllable growth lever. Without it, scaling intelligence feels like gambling. With it, autonomy becomes something the business can trust, that difference is subtle and decisive.
Closing perspective: visibility is the real scaling constraint
Agentic AI platforms rarely fail because the underlying ideas are weak or the models are incapable. In most cases, the technology works exactly as designed. Agents reason, coordinate, and adapt. Demos are impressive. Early results are encouraging.
The real strain appears elsewhere when system complexity grows faster than the organisation’s ability to observe and explain it.
From a technical standpoint, this shows up as opaque execution paths, unpredictable latency, and costs that accumulate across agent chains in ways that are difficult to attribute. Engineers see symptoms but struggle to isolate causes. Optimisation becomes reactive rather than intentional.
It turns agent execution into something measurable, intent by intent, agent by agent, decision by decision. It exposes where reasoning adds value, where it introduces friction, and where autonomy needs boundaries.
From a business perspective, the same problem manifests differently. Unit economics become fuzzy. Experimentation slows because outcomes are harder to predict. Leadership confidence erodes, not because AI is failing, but because it cannot be governed with clarity. Observability is what closes this gap.
At a business level, it transforms autonomy from a leap of faith into a controllable system. Costs become explainable. Trade-offs become explicit. Decisions shift from instinct to evidence.
This is what separates an impressive prototype from a dependable product. It may still be early days for agentic AI, but the pattern is already visible. Platforms that invest early in observability scale with confidence and calm. Those that postpone it often find themselves slowing innovation, not because the technology failed, but because they can no longer see clearly enough to trust it.
In agentic systems, visibility is not a support function. It is the foundation that allows intelligence to scale responsibly, sustainably, and at pace.