Bezel Workflow intent
internal prototype · canonical JSON + Dreamborn Forge HTML
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intent · supabase_json

Bezel Workflow intent

Workflow automation tools were built around brittle trigger/action chains and human-defined step logic. AI agent workloads need durable, observable, capacity-aware orchestration where agents claim work, route around failure, and adapt to changing APIs.

Planning Surface

Use this to decide what happens next.

Status

draft

problem

Workflow automation tools were built around brittle trigger/action chains and human-defined step logic. AI agent workloads need durable, observable, capacity-aware orchestration where agents claim work, route around failure, and adapt to changing APIs.

target users
  • Operations teams replacing brittle Zapier/n8n automations
  • AI-native companies running agent-driven internal operations
  • Technical workflow builders who need connector breadth without building every integration
  • Platform teams that want Temporal-like reliability without hand-building connector logic
success metrics
  • A nontechnical user can define a Flow that fans out to at least three integrations and observe completion status.
  • Multiple agents can claim from one integration topic without double-processing a job.
  • A connector can survive an unexpected but interpretable API response change without breaking the Flow.
  • A failed or expired claim returns safely to the topic and is reclaimed by another compatible agent.
  • First demo workflow processes 200 invoice-like events/day with visible parallel status and no lost jobs.
Agent Handoff
Start Here

Workflow automation tools were built around brittle trigger/action chains and human-defined step logic. AI agent workloads need durable, observable, capacity-aware orchestration where agents claim work, route around failure, and adapt to changing APIs.

Completion Evidence
  • A nontechnical user can define a Flow that fans out to at least three integrations and observe completion status.
  • Multiple agents can claim from one integration topic without double-processing a job.
  • A connector can survive an unexpected but interpretable API response change without breaking the Flow.
  • A failed or expired claim returns safely to the topic and is reclaimed by another compatible agent.
  • First demo workflow processes 200 invoice-like events/day with visible parallel status and no lost jobs.
Problem

Workflow automation tools were built around brittle trigger/action chains and human-defined step logic. AI agent workloads need durable, observable, capacity-aware orchestration where agents claim work, route around failure, and adapt to changing APIs.

Success Metrics
  • A nontechnical user can define a Flow that fans out to at least three integrations and observe completion status.
  • Multiple agents can claim from one integration topic without double-processing a job.
  • A connector can survive an unexpected but interpretable API response change without breaking the Flow.
  • A failed or expired claim returns safely to the topic and is reclaimed by another compatible agent.
  • First demo workflow processes 200 invoice-like events/day with visible parallel status and no lost jobs.
Structured Payload

Machine-readable source fields

kind

intent

problem

Workflow automation tools were built around brittle trigger/action chains and human-defined step logic. AI agent workloads need durable, observable, capacity-aware orchestration where agents claim work, route around failure, and adapt to changing APIs.

project

Bezel Workflow

target users
  • Operations teams replacing brittle Zapier/n8n automations
  • AI-native companies running agent-driven internal operations
  • Technical workflow builders who need connector breadth without building every integration
  • Platform teams that want Temporal-like reliability without hand-building connector logic
schema version

intent.product.v1

non negotiables
  • Queue-first execution: work is claimed, not merely fired.
  • Router Agent persists throughout a Flow and decides next steps from completion evidence.
  • Connectors are agent-backed and resilient to API response drift.
  • Credentials are scoped per workflow and stored in a vault; raw secrets are never stored in workflow rows.
  • Parallel fan-out and sequential workflows use the same Flow Engine abstraction.
  • Every claim, completion, failure, and routing decision is observable and audit-friendly.
out of scope v1
  • Marketplace billing for third-party connector authors
  • Full Zapier-scale connector catalog
  • User-facing agent marketplace
  • Advanced visual branching/loop UX beyond the first builder surface
success metrics
  • A nontechnical user can define a Flow that fans out to at least three integrations and observe completion status.
  • Multiple agents can claim from one integration topic without double-processing a job.
  • A connector can survive an unexpected but interpretable API response change without breaking the Flow.
  • A failed or expired claim returns safely to the topic and is reclaimed by another compatible agent.
  • First demo workflow processes 200 invoice-like events/day with visible parallel status and no lost jobs.
product category

AI-native workflow orchestration platform

source memory id

99edd8d2-5780-43a2-bbd0-03982353768a

initial killer use case
name

Invoice Processing Fan-Out

description

Each invoice arrival routes in parallel to Airtable write, HubSpot update, and Gmail notification. Router collects completion events and advances or remediates based on actual claim/completion state.