How Do AI Agents and Humans Work Together in Enterprise Operations?
An enterprise agent meets a case outside its scope, then stalls or repeats one action until a step limit stops it. Practitioners in forums such as r/AI_Agents report versions of this failure in production. Well-designed enterprise agentic AI handoffs treat that moment as a routing decision. The agent pauses and passes the case, with its state intact, to a person or a supervisor agent.
Why do enterprise AI agents hit dead ends?
Agents stall when their instructions describe a different process from the one operators run. Teams often build agent instructions from standard operating procedures (SOPs), process documentation, and system event logs, the timestamped records that software keeps of its own activity. Those inputs record the designed path and the activity that instrumented systems capture, but they can omit the exception paths, spreadsheet workarounds, and cross-application handoffs that operators use on hard cases.
Event logs miss much of that work. In one study, a log-based detection method found 8 of the 51 workarounds that earlier researchers had identified through observation and interviews (Weinzierl et al., 2022).
Gartner predicts 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). Teams should define an exception path as part of their operational risk controls.
What happens when an agent encounters an exception
In agentic workflow automation, exceptions are the cases a well-designed agent detects as outside its defined scope, and the agent stops acting on them and routes each one with its working state attached. Without that design, agents can loop. The AgentBench authors report that in 90% of runs that exhausted the task limit, the model had started repeating content it generated earlier (AgentBench, 2024).
The other outcome is silent failure. An insurance agency owner on r/AI_Agents described a bot that met a client frustrated about a claim. The owner wrote that it “kept trying to collect intake information instead of recognizing the situation needed a human” (r/AI_Agents).
For agentic AI failure handling, a designed exception path replaces both outcomes with explicit steps:
- Detect: The agent finds that the case matches no permitted path, that a confidence or policy check has failed, or that the same tool call repeats.
- Pause: The agent stops writing to systems of record and holds its working state.
- Route: The case goes to a named human queue or a supervisor agent, depending on the trigger.
- Record: The log captures what the agent attempted and why it stopped.
How do AI agents and humans share work in a hybrid operating model?
In a hybrid operating model, agents and people work the same case and pass it back and forth at defined handoff points. In a property claim, an agent collects documents and checks coverage, an adjuster decides liability, and the agent then drafts correspondence and updates the claim system. The adjuster approves any payment above the agent’s authority.
Nearly 80% of enterprises still run agentic systems in assisted or supervised modes, with humans retaining approval authority over key decisions (HFS Research, 2026).
Hybrid workforce AI agents need a shared case record that both people and agents read. That record shows who owns the case at each step, the exception paths, the handoff points, and any pending approvals. The case has one named owner at a time, and ownership changes at each handoff. Skan AI calls this shared record the Context Graph of Work.
What triggers a handoff in each direction?
Each enterprise sets its own trigger values, because NIST’s AI RMF states that human judgment should determine “the precise threshold values” for trustworthiness metrics (NIST, 2023). Treat the settings below as illustrative starting points for a claims or lending queue:
| Trigger | Agent to human | Human to agent | Illustrative setting |
|---|---|---|---|
| Confidence | Extraction or classification falls below threshold | Reviewer confirms the field and returns the case | Below 0.80 on a payment-relevant field |
| Risk tier | Policy classes the action as high-risk | Reviewer clears the risk and delegates low-risk steps | Read-only steps autonomous; writes need approval |
| Reversibility | Action cannot be undone | Approval returns with the case | Every payment, deletion or external letter |
| Policy limit | Amount exceeds delegated authority | Approved amount returns to the agent | Above the agent’s payment limit |
| No progress | Identical tool call repeats | Reviewer removes the blocker | Configured retry limit reached |
| SLA timeout | Reviewer misses the deadline | Not applicable | Matched to regulatory clocks |
CMS sets a hard limit for that last row. Under CMS-0057-F, impacted payers must decide expedited prior authorization requests for medical items and services, excluding drugs, within 72 hours (CMS, 2024).
Designing human handoff vs. supervisor escalation
Route to a supervisor agent when the problem is coordination. Route to a person when the problem involves judgment, authority or risk. A supervisor agent can reassign a subtask, retry with a different specialist or re-plan. Microsoft’s Azure Architecture Center describes an AI agent handoff to human support staff “when the problem is important to solve but no AI agent currently has the capabilities to address it” (Microsoft).
In either case, the payload has to let the recipient continue without restarting. The payload below shows an illustrative claims handoff:
Keeping decision rights with humans
Governed autonomy assigns each action a level and holds high-impact actions for approval. Gartner describes a level at which agents act only after explicit human approval for every action. At the highest level, agents act within guardrails while humans review exceptions, audit logs, and aggregated outcomes (Gartner, 2026). 3 controls hold that structure in place:
- Approval for high-impact actions: OWASP advises using human-in-the-loop control “to require a human to approve high-impact actions before they are taken” (OWASP, 2025).
- Least-privilege access: Agents get only the permissions their task needs. Downstream systems, rather than the model, enforce authorization.
- Attributed audit trail: ISACA recommends logging every AI action with “whether it was a human, an application, or an AI agent, along with the reason for it” (ISACA, 2025).
What are agentic operating procedures (AOPs) and how do they prevent failure loops?
Agents need a defined path for each known exception, and process mining reaches end-to-end depth only after integration with every system of record while missing work that never reaches an event log. Task mining captures individual tasks without the end-to-end process.
Machine-readable operating procedures for agents give an enterprise a defined path for each known exception. Agentic operating procedures (AOPs) set out the workflow steps, decision rules, exception handling, escalation thresholds, and success criteria in machine-readable form. Teams derive them from observed work, including the exception paths that manuals omit.
AOPs stop loops in three ways, each of which replaces another attempt with a defined rule, route, or stopping point, as the list below shows:
- The procedure defines stopping conditions and escalation points in advance, so an out-of-scope case triggers a rule instead of another attempt.
- Teams derive the procedure from the exception paths operators handle, so each known exception has a defined route.
- Success criteria let the agent recognize when a task is complete and stop there.
How does process intelligence prevent failure patterns before they happen?
Process intelligence prevents failure patterns by working from observed work rather than documented procedure, and Skan AI builds the shared case record described above as the Context Graph of Work. That record is a continuously updated graph that connects people, applications, tasks, decisions, and policies, drawn from observed work, and it gives teams an operational ground truth that reflects how real cases move across systems and people.
When a team needs to see work across legacy systems without long integration projects, Skan AI combines end-to-end process depth with zero-integration capture. Skan Virtual Assistant (Skan VA) captures process activity across mainframes, virtual desktop infrastructure (VDI), legacy desktop applications, web applications, and spreadsheets, with privacy by design.
Under this privacy by design model, the Skan VA is designed not to capture email content or keystrokes, and Skan PRISM, the component that redacts protected information from screens pixel by pixel, does so inside the customer environment before the information leaves it.
Skan Intelligence turns this work context into end-to-end flows, variant analysis, and drift detection. Variant analysis compares the different paths cases take, and drift detection flags when work departs from the expected procedure. Teams can see new exception paths and rework loops, then update procedures as those patterns appear.
Skan AI Agents generates AOPs from observed work and evaluates the resulting agent behavior against human baselines. On the Skan AI agentic AI platform, Skan AI Agents execute within those procedures, and Agent Studio defines each agent’s scope, permissions, boundaries, and escalation paths before launch.
ISACA’s audit guidance calls for attributed logs and approval records (ISACA, 2026), alongside tamper-evident trails (ISACA, 2026). Compliance teams can map Skan AI’s governance records to those expectations and to the EU AI Act:
- Skan AI’s governance layer records inputs, actions, decisions and rationales for case-level review.
- EU AI Act Article 12 requires high-risk systems to support automatic recording of events.
- Article 14 requires high-risk systems to enable human overseers to override or reverse outputs, as appropriate and proportionate.
- For Annex III systems, including credit scoring and life and health insurance pricing, these obligations apply from 2 December 2027 (EU AI Act Service Desk, 2026).
Plan your handoff design with Skan AI
Enterprise handoff design needs explicit oversight steps, escalation triggers, compliance records and fallback routing when reviewers miss deadlines.
Get a Skan AI demo focused on the processes and operating outcomes your organization needs to transform most. See how our platform helps you capture and redesign handoffs before you scale your agents so each exception has a defined route and a named owner before an agent ever deploys.
Frequently Asked Questions
Build oversight into the workflow as designed steps, each naming a trigger, a destination queue, a decision owner and the evidence that travels with the case. Give every agent action an autonomy level, hold high-impact actions for approval and record each approval in the case log. Review override and escalation rates on a regular schedule and adjust thresholds accordingly.
Escalate whenever the agent lacks a rule, a permission or enough confidence to act; it should never loop or act blind. A logged hard stop is acceptable only when abandoning the action is safe. Repeated identical tool calls, missing inputs and policy conflicts should trigger escalation after a defined retry count.
Defensible handoffs rest on four records: who held decision rights for each action, a tamper-evident log of agent and human steps, an identity for every actor including the agent, and evidence of human approval wherever approval was required. Skan AI’s assurance documentation attests only to Skan AI’s own controls, so the enterprise still has to produce the 4 records.
Every handoff needs a response deadline, a fallback destination and a logged outcome. When the reviewer misses the deadline, route the case to a secondary reviewer or supervisor and keep the pending agent action blocked.
