rewrite this title AI Agents Are Doing Real Work. Can You See What They’re Doing?

In the old world, if something in your business went off the rails, you could usually find it by following noise.

A customer complained.

A server alarmed.

A manager noticed someone doing something odd.

Now we’re rolling out autonomous AI agents that do real work at machine speed, across multiple systems, with no facial expression, no body language, and no “hey, this looks wrong” instinct.

They’re productive. They’re tireless. They’re also, in operational terms, an invisible workforce.

Why AI Observability matters

Back when “observability” was just a nice-to-have, it meant checking whether servers stayed awake and whether applications kept answering politely.

Now we’re asking something stranger: whether a system that never sleeps, never complains, and never admits uncertainty is quietly doing the wrong thing at industrial speed.

That shift matters because enterprises aren’t just deploying AI features anymore. They’re deploying agents—software actors that take actions across systems, touch data, and make decisions that look an awful lot like work. And unlike human workers, agents don’t leave visible evidence unless we design it in.

You can monitor whether the infrastructure is up, but you can’t monitor whether the agent’s logic is sound.

AI Observability: what it means now (and why UC teams are pulled into it)

AI observability is emerging to solve a simple but uncomfortable problem: autonomous agents do real work, yet their work is inherently hard to observe. That makes it difficult to answer basic questions enterprises will need to answer in production.

What is the agent doing right now? Why did it make that decision? Did it remain inside policy guardrails? Is performance degrading or drifting over time? Can we prove to auditors and regulators what happened?

This matters in UC and contact centre environments because so many agent workflows are built on communications data—calls, chats, emails, meeting transcripts, routing decisions, escalation paths, and follow-up actions. If an agent is summarising a call, drafting a customer response, or triggering a workflow based on a conversation, then the “ground truth” evidence often lives in the communication layer.

And that creates a practical reality for two audiences at once.

For enterprise IT/security, the question becomes: how do we supervise probabilistic software that acts across systems without leaving the kind of logs we’d accept for any other production process?

For service providers and MSPs, the question becomes: can we turn that supervision requirement into a durable service line—monitoring, governance, compliance reporting, and incident response—rather than watching margins collapse as per-seat models deflate?

If you can’t answer “why did it do that?” you don’t have observability—you have hope.

Why traditional monitoring stops short

Classic observability stacks—metrics, logs, traces—were designed around deterministic systems and failures we can name. You can tell when a CPU is overloaded, when latency spikes, or when a service throws an error.

Agentic systems break that neat model. An agent can complete a workflow successfully while making a series of decisions an organisation would consider unacceptable if it could see them.

It may interpret intent incorrectly while still producing a fluent response. It may overreach permissions without tripping an infrastructure alarm. It may “drift” as prompts, tools, data sources, and feedback loops evolve. It may create compliance exposure without any single catastrophic failure.

So enterprises end up in a new kind of operational posture: the systems run, but the decisions are opaque.

That opacity becomes dangerous in the exact scenarios enterprises are now targeting with agents: workflows with authority—customer communications, ticket routing, data access, refunds, account changes, scheduling, and internal approvals.

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AI Observability in the enterprise: the questions vendors will have to answer

When buyers say “observability,” they’re rarely asking for another dashboard. They’re asking for control.

An agent that acts across five systems needs a single narrative of its actions, not five disconnected logs.

1) Real-time activity monitoring across systems

Agents don’t work in a single UI. They move across CRMs, ticketing systems, knowledge bases, UC clients, contact centre tooling, and identity layers.

What enterprises need is a live view of agent actions: which tools were called, which data was accessed, what was changed, what was sent externally, and what was queued for review.

This is the difference between “the agent is enabled” and “the agent is behaving.”

2) Reasoning-chain visibility (explainability you can operationalise)

Explainability gets discussed like it’s a philosophical requirement. In practice, it’s an incident response requirement.

When something goes wrong, security and IT need to reconstruct what input the agent saw (including conversation context), what policy rules were applied, what tools were invoked, what intermediate outputs were produced, where uncertainty showed up (if anywhere), and what the final action was and why.

If that chain isn’t recorded, debugging becomes guesswork—and governance becomes a PDF policy that nobody can enforce.

3) Guardrails, policy enforcement, and compliance boundary monitoring

Enterprises aren’t just worried about “bad answers.” They’re worried about bad actions: data leakage, unauthorised access, discriminatory outcomes, policy breaches, or violations of regional requirements.

Observability needs to attach to policy controls in a way that’s testable and alertable. Otherwise you get the worst of both worlds: rules on paper, and agents in production.

4) Drift detection as an operational discipline

Drift isn’t only model drift. In agentic systems, drift can come from tool changes (new API behaviour), prompt changes, new data distributions, updated knowledge sources, feedback loops from human corrections, and new business policies not reflected in agent constraints.

That makes drift detection less like “monitor the model” and more like “monitor the system-of-systems.” The operational question becomes: are outcomes shifting, and can we tie the shift to a controllable cause?

5) Audit trails that satisfy regulators and customers

At some point, someone will ask: prove it didn’t violate GDPR, prove it didn’t discriminate, prove it didn’t access data it shouldn’t have.

If the only evidence is a few snippets of conversational text, that’s not an audit trail. Observability platforms—and the governance programmes around them—need to create traceable, time-stamped, tamper-resistant records of what happened.

In communications-heavy environments, that also means preserving the interaction context that drove the decision.

Why conversation data becomes the backbone for AI Observability in UC

Many enterprise agents are, effectively, conversation processors. They interpret human intent, summarise, route, recommend, and respond.

That’s why the communications stack becomes central: it’s where the signal originates and where the consequences land. Calls, chats, and transcripts aren’t just “content.” They’re inputs into decisions with operational and regulatory implications.

A practical implication: service providers often already hold the raw materials for observability—recordings, transcripts, metadata, routing outcomes—but not in a structure designed for tracing agent behaviour across time and systems.

This is where standardisation efforts like vCon start to matter. The promise is straightforward: structured conversation records that make it easier to answer who said what, when, in what context, and which systems (or agents) processed it. That structure is what turns “we have the transcript somewhere” into “we can reconstruct the decision chain.”

In an agentic enterprise, the transcript isn’t just evidence—it’s part of the control plane.

Why AI Observability spend rises as AI replaces seats

For years, providers have worried about per-seat deflation: if AI does more work, traditional licensing can shrink.

Observability flips the incentive. As organisations deploy more agents, the surface area for monitoring expands: more workflows to instrument, more tools and data sources to connect, more policies to enforce, more events to store and search, and more audits and reviews to produce.

That’s why “AI Observability” and “AI Governance-as-a-Service” are being discussed as sticky, high-margin offerings—especially for service providers and MSPs who can bundle monitoring, reporting, and incident response into recurring contracts.

For enterprise buyers, the economic argument is simpler: if agents are going to operate at machine speed, the cost of not seeing what they’re doing is likely to arrive as an incident—security, compliance, customer trust, or operational disruption.

The uncomfortable truth is that many organisations will fund observability only after the first near-miss. The smarter move is treating it as foundational infrastructure, not an add-on.

The first time an agent makes 10,000 small mistakes, you’ll wish you’d budgeted for seeing 10,000 small warnings.

How IT and security teams can evaluate AI Observability without buying hype

To cut through noise, enterprise teams can pressure-test vendors and internal programmes with a few operational demands.

Reproducibility: can we replay the agent’s inputs and tool calls for a specific incident?
Traceability: can we link a customer interaction to agent reasoning to system actions across platforms?
Policy evidence: can we prove guardrails were applied at decision time, not just documented?
Change accountability: can we see when prompts, tools, permissions, or knowledge sources changed—and what behaviour changed with them?
Action controls: can we throttle, pause, or roll back agent capabilities safely when risk rises?
Human review: can we route high-risk decisions to approval without destroying workflow speed?

These aren’t theoretical. They’re the difference between an agent you can operate and an agent you merely tolerate.

Plausible future drift: un-auditable work becomes normal

The likely future isn’t a dramatic AI catastrophe. It’s a slow cultural shift.

As agents become normal, organisations will start accepting “the system decided” as a legitimate explanation—at least internally. Teams will move faster, metrics will improve, tickets will close, and the machine will keep working.

Then the incentives tighten. More automation. Less human review. Shorter cycles. More tool access. A little more authority granted to the agent because it “usually works.”

And eventually, a new baseline forms: work happens, outcomes appear, and nobody can precisely explain the path from input to action without an expensive forensic exercise. Not because anyone hid it—but because nobody built the scaffolding to see it.

That’s the real drift to watch: not whether agents get smarter, but whether enterprises quietly decide that understanding is optional.

Automation you can’t explain doesn’t stay “efficient” for long—it becomes a liability on a schedule you don’t control.

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FAQs: AI Observability

What is AI observability?

AI observability is the ability to monitor, trace, and explain how AI systems—especially agents—behave in production. It goes beyond uptime and performance to capture decision context, tool use, policy enforcement, and auditable records of actions.

Why can’t traditional observability tools monitor AI agents properly?

Traditional tools are excellent for deterministic failures (latency, error rates, infrastructure health). Agents can “succeed” operationally while making flawed or risky decisions, so you need visibility into reasoning chains, guardrails, and decision outcomes—not just system metrics.

What does “debugging an AI agent” actually involve?

Debugging an agent means reconstructing what inputs it saw, which tools it called, what intermediate outputs it generated, and why it chose the final action. Practically, it requires trace logs, replay capability, change tracking, and a clear link between policies and decisions.

How do enterprises detect AI drift in agentic systems?

Drift detection typically involves monitoring outcome patterns over time and correlating changes with updates to prompts, tools, knowledge sources, permissions, or data distributions. For agents, drift often comes from system changes around the model, not only the model itself.

How can AI observability help with GDPR, audits, and compliance?

Observability can produce time-stamped, traceable records that show what data was accessed, what decisions were made, and what controls were applied. That evidence is critical for audits, regulatory inquiries, and internal governance reviews—especially when agents interact with sensitive data.

How far could AI observability realistically go if left unchecked?

If enterprises treat observability as purely a control mechanism, it could evolve into pervasive monitoring of every interaction, decision, and worker override—creating a “compliance-first” culture that optimises for defensibility over judgment. The risk isn’t dystopia; it’s a quiet trade where speed and certainty are bought with heavier surveillance and reduced discretion.