In short
- Resolve is the governed execution layer that turns requests, alerts, and AI decisions into action across enterprise IT systems.
- AgentLab handles ambiguity and context; Workflow Designer controls repeatable execution; Audit Trail makes activity visible and auditable.
- The operating rule is simple: use AI where judgment adds value, and use deterministic workflows wherever reliability, permissions, and proof matter.
Governed AI resolution requires both the non-deterministic reasoning capabilities of LLMs combined with a deterministic automation that determines where reasoning should end, where controlled execution should begin, and how to prove the result. Resolve provides that system.
{{deeplink:359685:[AgentLab]:agentlab}} interprets requests and operational context, works through ambiguity, and identifies the right next action. The {{deeplink:359685:[Workflow Designer]:workflow_designer}} turns that action into a defined sequence with controlled execution logic. {{deeplink:359685:[Audit Trail]:audit_trail}} provides the activity-level record teams need to review outcomes and investigate exceptions. Together, they give enterprises a practical path from AI reasoning to governed action across IT systems.
| AI reasoning | Deterministic workflow |
|---|---|
| AI reasoningInterpret intent, work with incomplete information, gather context, and choose the next approved action. | Deterministic workflowExecute defined steps, apply controls, manage failures, log activity, and test whether the action worked. |
One Resolve Model for Deciding What AI Should Do
The division of labor and the risk boundary are the same design decision: AgentLab reasons, Workflow Designer executes, and Audit Trail provides evidence. Use these five questions to apply that model to any process you automate in Resolve.
| Boundary question | Design decision | Resolve capability |
|---|---|---|
| Boundary questionIs the input ambiguous? | Design decisionUse AI to interpret natural language, correlate allowed context, or ask a clarifying question. | Resolve capabilityAgentLab |
| Boundary questionCould the action affect access, production, or a critical service? | Design decisionSend execution through a reviewed workflow with explicit permissions, safeguards, and approvals. | Resolve capabilityWorkflow Designer |
| Boundary questionCan success be defined? | Design decisionAdd a validation step that checks system state or confirms the user’s outcome. | Resolve capabilityWorkflow Designer |
| Boundary questionWhat can fail? | Design decisionDefine expected errors, timeouts, retries, compensating actions, and escalation paths before automating. | Resolve capabilityWorkflow Designer and Audit Trail |
| Boundary questionWhere is human judgment required? | Design decisionKeep approval or escalation points for policy exceptions, material risk, or weak evidence, with prior activity available for review. | Resolve capabilityWorkflow Designer and Audit Trail |
The boundary between deterministic and non-deterministic should reflect the risk of the action, the quality of the available context, and your ability to verify the outcome.
A VPN Request from Intent to Resolution with the Resolve Platform
Suppose an employee says, "My VPN isn't working." The request is simple to read but incomplete. The issue could involve a password, an expired certificate, a device configuration, the network, or the VPN service itself. A governed resolution flow can separate the uncertain parts from the repeatable ones.
- A Resolve VPN agent interprets the request in AgentLab
Resolve’s AI identifies the user's intent, recognizes that the issue concerns VPN access, and asks for missing information when necessary. At this stage, it’s reasoning about the request rather than changing a system.
- The Resolve workflow gathers trusted context
The AI or an approved workflow collects the inputs your process requires, such as the user's identity, device, recent authentication result, relevant service status, and ticket history. Your team decides which sources are trusted and which data the process may use.
- The Resolve workflow executes
Once the likely issue and required inputs are clear, the AI chooses from workflows that your team has already reviewed. It does not invent a new production procedure during the interaction.
- The Resolve workflow runs the controlled sequence
The workflow runs the diagnostic and remediation steps in a defined order. It can branch on known conditions, require approval before a sensitive change, and stop or escalate when a check fails.
- The Resolve workflow verifies the outcome
The workflow should test the result: Can the user authenticate? Is the expected configuration present? Has the relevant service recovered? Verify that the issue was truly resolved.
- The Resolve workflow closes the loop or escalates
If the checks pass, the process updates the record and communicates the outcome. If uncertainty remains, it hands the case to a person with the evidence already collected, so the technician can focus on the exception instead of repeating the initial triage.
How Resolve Makes This All Work in Practice
At Resolve, we use AI reasoning to interpret requests and operational context, then hand execution to deterministic workflows. Resolve's Workflow Designer supports defined execution logic and error handling. Teams can assign workflow permissions, and operators can follow workflow activity through the Audit Trail. These controls help teams apply the same reasoning-and-execution pattern across service desk and IT operations processes.
This builds a resolution process that can understand a request, take an approved action, verify the result, and involve a person when the evidence or risk demands it. When you design those boundaries deliberately, AI becomes part of a governed operating model rather than an uncontrolled shortcut to production.
To see how this works in practice, try out the Resolve Platform Experience {{deeplink:359685:[here.]tour_url}}
Frequently Asked Questions
What is governed AI resolution?
Governed AI resolution uses AI to interpret requests and context, then routes execution through approved workflows with permissions, safeguards, validation, auditability, and defined points for human review.
Why not let an AI agent execute the full process?
AI is useful when the input is ambiguous or the next action depends on context. Deterministic workflows are better suited to production actions that require predictable logic, controlled permissions, known failure handling, and a verifiable outcome. Resolve combines both.
How do AgentLab, Workflow Designer, and Audit Trail work together?
AgentLab interprets the request and context. Workflow Designer runs the approved sequence and its controls. Audit Trail provides visibility into workflow activity, supporting review, governance, and informed escalation.
Where should a human remain involved?
Keep human approval or escalation where policy exceptions, material risk, weak evidence, or an undefined success condition require judgment. The Resolve workflow should make those handoffs explicit rather than treating them as afterthoughts.