Intent Understanding: Why This Packet Exists#

Structural Tension#

Current Reality:
Agent teams orchestrate work via ad-hoc dispatch patterns; workflow state is fragmented across logs, tickets, and memory. Human designers discuss workflow intent in prose; agents read unstructured data or make assumptions about task dependencies.

Desired Outcome:
Agents operate on a shared, queryable workflow state store (MantisBT + MCP) where task graphs, dependencies, and execution history are legible to both humans and agents. Design conversations and agent operations are grounded in the same academic frameworks.

Why Now#

The convergence of three fields — workflow orchestration (LangGraph), agent communication (MCP standardization), and process mining (BPM → LLM fusion) — creates an opportunity to build agent-native workflow management on recognized academic foundations rather than reinventing patterns.

Intended Audiences#

Architects — understand which academic fields ground each design choice
Agent Developers — know what state to query from MantisBT and how it maps to workflow concepts
Cross-Repo Stewards — reference this packet when evaluating agent orchestration decisions in Miadi, coaia-agent, or IAIP

Decisions This Packet Strengthens#

Should MantisBT be the state store? — Yes; it aligns with BPM and process mining traditions
Which protocol for agent-MantisBT access? — MCP; it's becoming the standard for agent-to-service communication
How to encode workflow dependencies? — MantisBT relationships (parent/child, related); readable by agents via REST API
What success looks like — Agents traverse dependency graphs, query task state, and update issue notes without domain-specific glue code

Future Synthesis Points#

RISE framework specs for MantisBT integration (→ rispecs/)
Process mining queries on MantisBT execution logs (gap: needs custom plugin or script)
Supervisor agent that manages sub-agents via MantisBT task assignment (→ CLAUDES experiment)