Your AI Agent Just Made a Decision. great. do you know why?

The uncomfortable truth about the intelligence gap in modern AI systems.

We've spent decades building observability into our softwares. We have dashboards for latency, alerts for error rates, traces that follow a request through seventeen microservices and back. We know, almost to the millisecond, what our systems are doing.

It is very hard not to feel excitement at the prospect of deploying AI agents across your workflows; but then a nagging question emerges: how do you actually know why the agent made X decision?

This isn't a tooling problem. It's a thinking gap. We're applying a deterministic mental model to a system that is, by design, probabilistic. And until we fix that mismatch at the conceptual level, you’re building a flight recorder for a plane you don’t know how to fly.

The Illusion of Control

Traditional observability works because traditional software does exactly what it's told. A function receives inputs, executes logic, returns outputs. If you call “calculateTax(income)” you always get the same number back. The system has no judgement – it just follows the rules. You can trace execution paths and fully reconstruct why the system did what it did.

AI agents break all those assumptions.

Give an AI agent a goal — "research competitors and summarise pricing trends" — and it will decompose that goal, decide which tools to use, determine what information is relevant, and synthesise a response. Each of those steps involves a model making probabilistic choices. Run it again tomorrow and you'll get a subtly different result. Run it on slightly different data and you might get a dramatically different one.

What does your monitoring stack tell you about why the AI agent chose to call that API three times instead of once? Why it retrieved those five documents and not these seven? Why it summarised in that particular way?

Nothing. It tells you nothing.

Three Failure Modes Nobody Is Talking About Enough

1. Silent drift

In traditional software, bugs are usually loud. Exceptions are thrown. Errors are logged. Something breaks in a way you can see, and more often than not, you know about it immediately.

AI Agents fail quietly. An AI agent tasked with triaging customer support tickets might start de-prioritising a particular category of complaint — not because of a code change, but because of subtle shifts in how it's interpreting ambiguous requests. Your AI agent may be trained to treat “billing issues” as a high priority. But if customers start writing “I’ve been charged twice” in the ticket subject, your AI Agent may start treating those as lower-confidence matches to its high-priority category. It doesn’t know it’s under-prioritising billing complaints. There's no stack trace. No alert fires. The failure mode is a gradual, invisible degradation in judgement which is only visible in aggregate, after enough customers complain that their issue wasn’t resolved promptly.

AI agents that performed well in evaluation can start exhibiting systematic biases in production that only become visible weeks later, through downstream business metrics — not through any observability tool.

2. Compounding decisions in multi-agent systems

Single AI agents are complex. Multi-agent pipelines are a different class of problem entirely. When Agent A passes context to Agent B, which uses it to instruct Agent C, errors and assumptions don't just propagate — they compound. A slightly wrong inference in step one becomes a confidently wrong action in step three.

Let’s imagine here that Agent A picks up competitive intel from the usual web sources and passes it to Agent B to build a summary for the VP of Sales. If a key competitor decided to advertise new engineering jobs on a niche website that Agent A did not trawl, Agent B in charge of building the competitive summary will omit a crucial piece of intel – the competitor is supercharging their engineering capacity in a specific domain, signalling they may be expanding X capability. Agent B doesn’t know it is missing information, it only knows what it was given. It will therefore build a summary for the VP of Sales that states not much is happening at the competitor's. That same summary may be passed to Agent C who may suggest a strategic direction and investments based on a partial picture.

Tracing causality across agent boundaries is extraordinarily difficult with current tooling. You may end up with an audit trail that tells you what happened, but not really why.

3. The context window as a black box

Every time an AI agent “thinks”, it works from a context window – a finite set of information assembled at the moment of reasoning, which includes the task instructions, documents retrieved from various sources etc. The AI agent’s response – its decisions, its actions and finally its output – is entirely a function of what’s inside that window. Change the window, you change the answer. The context window is the reasoning environment. And in most production systems, nobody is watching it.

If you can’t see the context window, you can’t audit the decision and in regulated industries particularly, this has become a compliance problem.

These questions aren't academic. They're the difference between understanding a good decision and being able to reproduce it — and understanding a bad one before it recurs.

What Actually Helps

A few approaches that are worth exploring:

An AI Agent inventory as a foundation for governance. Before any sophisticated observability practices, organisations need something deceptively simple: a single, authoritative registry of every AI agent in production. This needs to be a live inventory that captures for each AI agent: what it’s designed to do, what systems and data sources it has access to, what decisions it’s authorised to make autonomously, who owns it and when it was last evaluated. The inventory is also where performance baseline lives and where ongoing monitoring takes place.

Semantic tracing, not just execution tracing. Now that you have all AI agents under one roof, it’s time to look at how they differ from traditional execution tracing. Let’s take an example; You prompt your AI agent to “review this contract and flag any clauses that represent unusual risk for us”. An execution trace will tell you:

·  Document retrieved: contract_v3.pdf ✓

·  Tool called: extract_clauses() ✓

·  Tool called: compare_to_standard_template() ✓

·  Output generated ✓

·  Total time: 4.2 seconds

A semantic trace will try to capture:

·  What the agent interpretation of “unusual risk”? Was it legal liability or financial risk?

·  Which clauses did it consider borderline but did not flag?

·  When it compared to the standard template, which other deviations did it notice but did not flag, and on what basis?

But How I hear you ask.

Decision checkpoints with human-readable rationale. Rather than adding semantic tracing after the fact, design agents to surface their reasoning at key decision points, i.e. here’s what I was asked to do, here’s what I retrieved, here’s what I considered, here’s what I rejected, here’s my answer and here’s my level of confidence”. This serves a triple purpose: it makes the system interpretable, and it creates natural intervention points for human oversight as well as building trust when stakes are high.

Behavioural baselines, not just performance baselines. Traditional monitoring asks: is the system fast and available? Agent monitoring needs to also ask: is the system behaving consistently with its intended purpose? This means building evaluation sets that test for behavioural drift, not just functional correctness — and running them continuously in production, not just at deployment.

Cross-agent context propagation standards. The industry needs — and is slowly developing — shared conventions for how agents pass context, flag uncertainty, and record their reasoning across boundaries. If you're building multi-agent systems today, invest in defining these standards internally before you scale.

Are we there yet?

Let’s be honest, most of what I’ve described in this piece isn’t yet standard practice. The field is moving fast but the observability infrastructure is lagging behind the deployment curve. Thankfully, the tooling is maturing fast as most organisations, researchers and regulators evaluating or implementing AI agents are asking the right questions.

The teams that will get this right aren’t those waiting for the perfect observability tool to appear. They are the ones choosing to embed observability within agentic AI design. They are the ones co-innovating with their partners and vendors now – while it’s hard and non-standardised – and they will come to something truly valuable: they’ll actually understand what their AI agents are doing. And this is not just a nice to have, it’s a competitive advantage in the age of AI.

Bottom line: Bake observability within AI agent design. Ask the hard questions to your vendors and partners. And never mistake a clean dashboard for actual understanding.