From Form-Filling to Agent-Driven: How Dewu Rebuilt Its Event Setup with a Two-Stage AI Workflow
This case study shows a pragmatic middle path between fully autonomous agents and manual workflows — exactly where most enterprise AI applications live. The architecture decisions (Workflow vs. Agent, interrupt/resume patterns, capability registries, progressive disclosure) are directly applicable to any team building AI-powered internal tools. It also demonstrates how to handle the hard parts: side-effect control, trust-building, and engineering trade-offs.
Dewu's community operations team faced a fragmented workflow: launching a marketing campaign required jumping between three systems, filling 40+ fields, and manually syncing changes. Their first attempt — an AI form-filler that pre-populated fields from a planning document — cut time but didn't change the paradigm. Operators still drove the process, and AI was just a smarter autocomplete.
The real breakthrough came when they flipped the model: AI drives the process, humans only confirm at key nodes. The second version used LangGraph to orchestrate a workflow that fetches documents, parses fields, creates topics and campaigns, and configures venue components — all while the operator reviews structured cards and approves or edits. A Capability Registry lets new scenarios plug in without touching core code.
The third version added a Stage 1 Agent that generates the planning document itself from a vague idea, using progressive disclosure to gather minimum info (name, time, topic) before handing off to Stage 2's aggregated workbench. The workbench reuses the existing venue previewer with an interactive overlay, supports natural-language component editing, and runs legacy forms in a separate runtime to avoid rewriting 16+ component configs.
The real lesson is that AI's ceiling in enterprise tools is not model capability but paradigm design — as long as humans remain the process driver, AI is just a faster form filler.
The Workflow vs. Agent decision is elegantly framed: if the process is a finite state machine, use Workflow; if it's an open-ended goal, use Agent. Most enterprise scenarios need a hybrid.
The interrupt/resume pattern is a practical implementation of human-in-the-loop that balances autonomy with control — it's not about limiting AI but about building trust through transparency.
The Capability Registry and Component Module Protocol demonstrate that extensibility in AI systems is less about model architecture and more about clean software engineering patterns (open-closed principle, plugin systems).
The progressive disclosure approach to information gathering is a smart UX pattern that reduces cognitive load — it's notable that they explicitly reference Nielsen Norman Group's work.
The Form Host compromise is a realistic engineering trade-off: sometimes it's better to wrap legacy systems than to rewrite them, even if it adds architectural complexity.
The permission escalation (read → write → publish) is a direct application of security best practices to AI systems — a pattern that should be standard in any agentic system.
The shift from 'AI helps you fill fields' to 'AI drives the process' mirrors the evolution of developer tools like Copilot to Copilot Workspace — the inflection point is when AI becomes the subject, not the tool.
The explicit rejection of fake progress bars and the emphasis on explaining 'why' at each interrupt point shows a mature understanding of AI trust-building — transparency beats performance theater.
The three immutable principles (controllable, accurate, lower burden) are a good checklist for any AI-powered internal tool — they prioritize operational reality over technical novelty.