WorkWise
A governed workflow router for company operations, AI agent actions, policy, approvals, auditability, and model cost control.
WorkWise is a workflow control layer for operational requests that involve money, risk, approvals, and audit requirements. It routes each step through a parser, rule engine, small model, strong model, or human approval so companies can reduce token waste while preserving policy enforcement, evidence, approval paths, and audit trails.
The repository represents an active MVP/prototype. Some product screens, system design details, and governance direction are being tested before commercialization.
- Defined structured action packages with extracted fields, missing data, risk factors, policy results, approval paths, recommended actions, evidence, and audit timelines.
- Designed the control plane around agent governance, tool permissions, action approvals, AI spend tracking, model routing, policy gates, and workflow reliability.
- Currently in development and testing to validate decision quality, cost savings, approval accuracy, and shadow-mode readiness.
Project Highlights
WorkWise acts as an AI Ops Control Plane across agents, models, tools, workflows, policy, approvals, spend, and audit events.
The architecture separates UI, application/control plane, gateway/SDK, integrations, and data/storage so governance stays in the operating layer.
An action passes through model/tool gateways, policy gates, approval decisions, execution, audit logs, trace, retry, and escalation.
The core surfaces include Agent Registry, Pending Approvals, AI Spend Dashboard, Workflow Trace, and Policy Center.
Video & Walkthrough
Timeline
Behind The Project
Not an operations chatbot
WorkWise starts from a practical constraint: many operational requests involve money, data, tool access, and approval responsibility. These requests need workflow governance, auditability, and approval paths, not just an AI answer.
Route by step instead of sending everything to a large model
A request is broken into steps. Parsers handle structure, rule engines handle deterministic checks, small models handle simple classification, strong models handle reasoning only when needed, and human approval keeps risky actions under review.
The action package is the decision unit
The output is not a chat response. It is an action package: extracted fields, missing data, risk factors, policy result, approval path, recommended action, evidence, and timeline. That gives reviewers a clear basis for approving, rejecting, requesting more information, or blocking.
Built for controlled testing
WorkWise is currently in development and testing. The right rollout path is shadow mode: let the system analyze and recommend alongside the existing process, measure cost savings, decision quality, and approval accuracy, then expand automation only where the evidence supports it.
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