What Is Work Observability? How It Powers Better AI Agents

Most large enterprises have funded AI programs that have not changed cost per case or processing time, and the operations leader who approved that spend now owns a gap nobody can explain. McKinsey’s State of AI 2025 reports that only 6% of companies see meaningful earnings before interest and taxes (EBIT) impact from AI investments (McKinsey, 2025). Agents grounded in documentation and system logs execute the process as written rather than the process as run.

Standard operating procedures (SOPs) describe the steps a process owner intended, and event logs record the steps an instrumented system happened to capture. Neither contains the spreadsheet reconciliation or the mainframe lookup that carries a case across six applications. Work observability supplies that missing input.

What is work observability?

Work observability is continuous, desktop-level observation of process activity across every application an operator uses, which lets an enterprise compare the documented process with the executed one and supply that evidence to the people and AI agents who run the work. The unit of analysis is the process, the task, the decision, and the exception, never the individual at the desk.

Each default discovery approach captures part of the picture. Process mining extracts a process model from event logs, the timestamped records an instrumented system writes when a case moves from one activity to the next, so it sees only what those systems log. Task mining records a timestamped sequence of user actions on a single workstation, which adds screen-level detail but still leaves the reasoning out.

A 2024 study in Information Systems on screenshot-based task mining found that user-interface logs capture clicks and keystrokes while “a major part of the human decision-making remains hidden” (Martínez-Rojas et al., 2024).

Software observability, as the Cloud Native Computing Foundation defines it, infers a system’s internal state from the logs and traces the system emits, and AI agent observability extends that to model calls and tool calls. Both describe the machine; neither describes the work the machine is supposed to take over.

The distinctions matter because each approach produces a different picture of the same operation:

How work observability differs from adjacent approaches

• Process mining — Data source: Event logs from instrumented systems. Leaves out: Work in applications with no structured event layer, manual handoffs, workarounds.
• Task mining — Data source: Timestamped user actions on one workstation. Leaves out: Decision reasoning, and cross-application flow when capture is episodic.
• Software and agent observability — Data source: Metrics, logs, and traces from code, model calls, and tool calls. Leaves out: Human work and the business process around the agent.
• Work observability — Data source: Desktop-level observation across all applications an operator uses. Leaves out: Email content and keystrokes, by design.

Why do AI agents fail without operational context?

AI agents fail in production because they execute the process the documentation describes while the operation runs a different one, and the difference sits in exceptions and judgment calls that no SOP or event log records. The agent is not hallucinating when it stalls on a case the SOP never anticipated; it is doing exactly what it was taught.

Gartner forecasts that over 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls (Gartner, 2025). Financial viability and risk control each depend on knowing the work before an agent takes it on. A program that cannot state the current exception rate for a process cannot price the agent, prove its value, or bound its risk.

McKinsey’s review of more than 50 agentic builds found that organizations “focus too much on the agent or the agentic tool” and track only outcomes, so when something goes wrong nobody can see which workflow step failed (McKinsey, 2025). The same review warns that without human oversight of accuracy, compliance, judgment, and edge cases, programs risk “silent failures, compounding errors, and user rejection.”

Take a Know Your Customer (KYC) review in a bank. The SOP describes a short sequence: pull the alert, verify identity documents, check sanctions screening, record the disposition. The analyst also opens the core banking system for account history and exports two screens into a spreadsheet to reconcile name variants. When the customer is a politically exposed person, the analyst emails a senior colleague, but an agent trained on the SOP never sees that work or the associated judgment call.

This gap predates agents. Deloitte’s 2017 survey of 424 respondents adopting robotic process automation (RPA) found that processes were not always well understood even where documentation existed, and that operations that looked standard in documentation differed significantly across countries and business units (Deloitte, 2017). Processes dependent on tacit knowledge are especially exposed when agents rely only on formal documentation.

The operational context problem has three parts: missing activity, stale evidence, and scoring that assumes one execution model.

The three context gaps that undermine enterprise AI

Each gap comes from how default discovery approaches collect and classify work.

• Invisible work: Mainframes, virtual desktop infrastructure (VDI), legacy desktop applications, spreadsheets, email, and chat may produce no structured event log that a log-based tool can read, so work performed there may never enter the process model.
• Episodic capture: Task-mining recorders that fire on individual user actions or refresh on a schedule, and consulting assessments that deliver a point-in-time baseline, can produce a picture that becomes stale before the agent built on it reaches production, and they may not detect drift after launch.
• Single-destination scoring: When a discovery tool is attached to one automation runtime, teams risk scoring every opportunity for that runtime, routing deterministic keying work and judgment-heavy exception work to the same engine instead of deciding which tasks belong to a bot, an agent, or a person.

How does observation become an agent?

An operations team turns observation into an agent through a sequence that moves from recorded process activity to a bounded operating procedure, with the agent tested against the human baseline before it writes anything to a system of record. This sequence targets agentic AI, meaning software that plans and executes multi-step work within set limits, rather than a scripted bot:

1. Observe the work. Record process activity across every application the case touches, with the unit of analysis on the process and not the person performing it.
2. Reconstruct the flow. Classify screen activity into tasks, stitch tasks into end-to-end cases, and separate the main path from its variants, exceptions, and rework loops.
3. Sort the work. Score each task for rule density, exception frequency, and judgment requirements, then use those scores to determine whether it fits deterministic automation, an agent, a hybrid design, or continued human execution.
4. Write the procedure. Encode workflow steps, decision rules, exception handling, policy constraints, escalation thresholds, and success criteria from the observed variants into a machine-usable procedure the agent executes.
5. Test, bound, and record. Simulate the agent against the observed human baseline, launch it with defined authority and escalation paths, and log every input and decision, including the rationale, for case-level review.

Step three is where programs often skip ahead. The argument for why agentic automation replaces rule-based bots applies to judgment work, while the argument for keeping a bot applies to high-rule-density work, and only observed scoring separates the two for a given task. A team that routes everything to one engine risks brittle bots on judgment work or expensive agents on keying work.

Step five is a loop rather than a gate. Suppose an agent recommends paying a water-damage claim, and the adjuster reverses the recommendation because the policy carries a gradual-seepage exclusion the agent’s decision rules did not cover. Once a supervisor approves that reversal, the supervisor adds a new decision rule to the procedure, and the next seepage claim escalates before payment rather than after.

What makes AI agent observability enterprise-ready?

Security reviewers require enterprise-ready observation to keep raw screen data inside the customer’s perimeter, redact protected information before anything leaves, cover mainframe and virtual desktop environments, and record every agent decision in a form a regulator or internal auditor can replay case by case. If a program fails any one of those tests, information security or model risk teams may reject it.

Enterprise teams often discover governance gaps only after an agent reaches production. Gartner forecasts that by 2027, 40% of enterprises will demote or decommission autonomous agents “due to governance gaps identified only after production incidents occur,” and attributes the failures to “binary governance,” uniform rules applied across every agent (Gartner, 2026).

Enterprise AI governance for agents therefore has to operate inside execution rather than in a quarterly review, through three controls:

• Defined authority for each action, so a payment above a threshold cannot proceed without a named human decision
• Explicit escalation thresholds derived from the exceptions operators already escalate
• A complete record of inputs, decisions, and rationale that an auditor can replay for any case

Reviewers assess four design requirements when deciding whether a desktop observation program can pass a works council and information security review:

• Unit of analysis: The observation layer classifies process activity and application interactions, not the performance of a named individual.
• Capture boundaries: Email content and keystrokes are never captured, and the compliance interface states that plainly.
• Redaction before transmission: Protected information is redacted at the pixel level inside the customer environment, and raw screen data stays inside the customer-controlled perimeter.
• Hosting choice: A fully customer-hosted deployment option covers data residency and sovereignty rules in banking, insurance, and healthcare.

Coverage is the final test. A team using an approach that needs a backend connector or source-system data preparation may spend substantial time on data engineering before it produces a process view, and the approach still cannot see the mainframe or VDI sessions where regulated work concentrates. Desktop-level observation captures those environments without backend connectors or application changes, and it requires no system downtime, though IT still participates in sensor deployment and security review.

Where does work observability deliver the most impact?

Operations teams gain the most from work observability in processes where a case crosses several applications, exceptions are frequent, and a human judgment sits between arrival and completion. These conditions describe KYC and anti-money laundering review, insurance claims, prior authorization and revenue cycle, accounts payable, and contact center work. Published evidence does not allow researchers to rank one of these functions above the others; published returns are modeled or vendor-commissioned, so the quantified operating problems are the better guide.

In anti-money laundering review, BCG reports that false-positive alert rates can reach or exceed 90% (BCG, 2025). Most analyst time therefore goes to cases that end in no action, and the disposition logic for closing them lives in analyst judgment rather than the rule book, which is exactly the context an agent needs and a log never holds.

The same pattern repeats across the other four processes:

Where documented and executed processes diverge in regulated operations

1. KYC and AML review — What arrives: Screening alert or periodic review in a case queue. Where it stalls: Reconciling identity data across core banking, screening, and document systems. Exception: Name variant or politically exposed person needing senior sign-off.
2. Insurance claims — What arrives: First notice of loss through a digital channel. Where it stalls: Policy verification on a mainframe and adjuster reconciliation of attachments. Exception: Coverage exclusion or litigation flag that routes to a specialist.
3. Prior authorization and revenue cycle — What arrives: Payer request or denial tied to a patient encounter. Where it stalls: Payer portal lookups and clinical documentation retrieval across systems. Exception: Medical-necessity question requiring clinician input.
4. Accounts payable — What arrives: Supplier invoice by email, portal, or scan. Where it stalls: Three-way match failure against purchase order and receipt. Exception: Price or quantity variance resolved by email with the buyer.
5. Contact center — What arrives: Inbound call or chat with a customer intent. Where it stalls: Representative switching between customer record, billing, and knowledge systems. Exception: Policy exception or complaint escalated to a supervisor.

Prioritize processes where the agent writes back to a system of record, meaning it records its result in the authoritative system rather than drafting a suggestion. Those processes give the human baseline and exception path measurable endpoints and produce an audit record.

Banking: Teams should prioritize Anti-money laundering (AML) and Know Your Customer review, where the disposition the agent records is the control a regulator will examine.

Insurance: Claims processing agents face the same test. The write-back is a payment or a denial, and every recommendation an adjuster reverses is an observable correction to the procedure rather than an anecdote.

Healthcare: Revenue cycle agents in a health system write back a prior-authorization submission or a corrected claim. The denial rate on those submissions, compared with the human baseline on the same payer mix, is the enterprise-owned measure.

Is your enterprise ready for observation-first AI?

An enterprise is ready for observation-first AI when its leaders can state the current cost per case and exception rate for one target process. They must also identify the decision points a human still owns from observed evidence rather than from interviews or an SOP. Most cannot yet.

In Deloitte’s 2026 readiness survey of 501 US leaders, only 5% said their business processes were highly prepared for AI agents, and respondents ranked business processes as the least prepared area (Deloitte, 2026).

Measurement lags as well. HFS Research found that 67% of enterprises still judge AI with productivity metrics designed for earlier automation waves (HFS Research, 2026). Enterprise-owned measures for an agent program look different:

• Share of case volume resolved without escalation
• Exception paths covered by the procedure, compared with exception paths observed in the baseline
• Accuracy against the human baseline on the same case mix
• Write-backs to systems of record completed without rework
• Portability of outcome data, procedures, and audit records in a vendor-neutral format

Fund the observation layer before the agent. The operating model needs collected outcome data to compare agent decisions with the human baseline.

Keep outcome data and procedures, including audit records, independent of any one model, and secure migration rights contractually. An enterprise builds its evidence base through accumulated cases, exceptions, decisions, and outcomes; its value depends on preserving that history independently of the execution provider.

See how Skan AI maps your operations

Skan AI is a context graph platform that observes process activity at the desktop level, across mainframes, VDI, legacy and web applications, spreadsheets, and office tools, and assembles that evidence into a Context Graph of Work that grounds both operating decisions and agent execution. It does not infer how a process runs from documentation or interviews; it builds the model from observed activity.

The Context Graph of Work is a continuously updated context graph whose nodes and edges carry attributes, connecting people, applications, tasks, decisions, policies, outcomes, relationships, sequences, and governance constraints. It captures dependencies, cross-system handoffs, documented-process gaps, and exception patterns from observed reality, which makes a digital twin of operations a fair description of it. Skan AI’s guide to business-process observability sets out the observation layer in more depth.

The platform’s collection, privacy, analysis, and agent capabilities divide into six parts:

• Skan Virtual Assistant, a lightweight desktop agent, scans work in selected processes and applications. Teams don’t need to make application changes, wait on system downtime, or deal with complex integrations. Email content and keystrokes are never captured. Neurosymbolic AI, which pairs neural perception of what is on screen with symbolic rules that classify it, preserves an explainable reasoning path from observed activity to each classification. Then, an operations leader can examine the evidence behind an output before using it in a production decision.
• Skan PRISM is an on-premises privacy and information security gateway that applies pixel-level redaction before protected information can leave the customer environment, so raw screen data remains inside the customer-controlled perimeter. Skan AI supports Skan-hosted and private-cloud deployment. It also supports fully customer-hosted deployment, which covers data residency and sovereignty requirements in regulated environments. Its assurance documentation covers ISO/IEC 27001, ISO 42001, SOC 2 Type 2, PCI DSS, HIPAA, GDPR, CCPA, and TrustArc.
• Three products run the five steps described earlier. Skan AI Intelligence handles steps one and two, producing end-to-end process flows, variant analysis, execution metrics, bottleneck detection, drift detection, and benchmarks from desktop-level observation. Skan AI Blueprint turns that evidence into a prioritized roadmap tied to measurable business outcomes, in weeks, not months. Skan AI Agents runs step five, launching agents with defined authority and escalation paths and recording each input and decision for case-level review against the human baseline.
• Automation Explorer performs step three through separate RPA Opportunity Score and Agentic Automation Score outputs. The scoring is vendor-neutral and separates deterministic work from work that requires context, reasoning, or judgment, so the enterprise routes each task to the right execution model rather than to one toolchain.
• Step four replaces the SOP with agentic operating procedures (AOPs), machine-usable representations of observed work that encode workflow steps, decision rules, exception handling, policies, escalation thresholds, execution rules, and success criteria. The Observation-to-Agent pipeline (O2A pipeline) converts observed work into governed agent behavior, with policy controls, evaluation against human baselines, and performance benchmarking.
• The capability buyers search for as agentic process intelligence is Skan AI Agents, which uses the Context Graph of Work and AOPs to define what an agent can do, when it must stop, and when a human must decide. Agent Studio sets scope, permissions, boundaries, escalation paths, and controlled launch conditions. The control and governance layer records inputs, outputs, actions, decisions, and rationales for case-level review.

Request a Skan AI demo to see how the platform can help your organization capture context and transform your specific processes across human and AI agent-driven execution.

Frequently asked questions

What is the difference between work observability and process mining?

Process mining reconstructs a process from the event logs an instrumented system records, so it sees only the steps those systems logged. Work observability observes process activity at the desktop across every application, including mainframes, spreadsheets, and legacy tools that produce no event log. It captures handoffs and workarounds, including exception paths that run between systems rather than inside them.

How is work observability different from AI agent observability?

AI agent observability traces what an agent did: model calls, tool calls, token usage, and latency, usually through OpenTelemetry-style spans. Work observability records how human operators perform the process before and alongside the agent. It is the upstream input that defines the procedure and the human baseline an agent is measured against, and agent observability then reports performance against that baseline.

Does desktop-level observation record what individual employees do?

Privacy-by-design observation keeps the unit of analysis on the process and the application interaction, not the person. Email content and keystrokes are never captured. Protected information is redacted at the pixel level before data leaves the customer environment, and identifiers are pseudonymized. Works councils and security teams should expect to review the capture boundaries and hosting model before deployment.

How quickly can an operations team get observed process data?

Initial operational insight arrives within a few weeks of deploying the desktop observation layer because the approach needs no backend connectors or application changes. Deployment requires no system downtime, though IT still participates in sensor deployment and security review.

How do we preserve operational context if we change model providers?

Keep outcome data and procedures, including audit records, independent of any one model, and secure migration rights contractually. The evidence base develops through accumulated cases, exceptions, decisions, and outcomes, and its value depends on preserving that history independently of the execution provider, so insist on export in a vendor-neutral format before the first agent launches.

Can work observability show whether an agent performs at the human baseline?

Yes. When the same observation layer records human execution and agent outcomes, operations teams can compare them on the same case mix. The comparison covers accuracy, share of cases resolved without escalation, processing time per case, and write-backs completed without rework. That evidence is what an operations leader needs before approving expansion from pilot to production.