What: The Agents and State Machines document defines 12 states and 25 invariants for Pi-sdk-based agents. This is a formal model — it needs to be validated against real agent traces.

Open questions:

Do all 25 invariants hold in practice, or are some unreachable/redundant?

Are there states missing from the model that real agents exhibit?

How does the model map to non-Pi-sdk agents (LangChain, CrewAI, custom)?

Evidence: State machines to visualize the work of agents, How to Background Agents

Next step: Collect state traces from Product Scout, Gardener, and any other running agent. Compare observed transitions against the formal model. Report violations.

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What: The Graduated Trust essay proposes a Web-of-Trust-based hop-distance scoring system for OSS contributions. But the real-world topology of OSS trust graphs is unknown.

NEW substrate evidence (2026-07-28): The Community Models in Seed document provides the actual Seed infrastructure for two trust models — formal communities (mutual membership, role-based permissions for contribution scoring) and user web of trust (one-way follows for discovery/filtering). This gives us a concrete architecture to map the research against:

Formal community membership → hop-0 (maintainers) and hop-1 (vouched contributors with role-based permissions)

Web of trust → hops 2+ (discovery paths through followed accounts)

The research question becomes: what's the empirical hop distribution of the web of trust for a typical Seed space?

Open questions:

What does the trust graph of a typical OSS ecosystem look like? (npm, PyPI, crates.io)

How many hops between a random contributor and a core maintainer?

Does the small-world property hold? Are there bridging nodes?

What path quality weighting makes sense empirically?

NEW: How do Seed's formal community boundaries map to hop-distance zones? Is a community member always hop-0, or are there sub-roles?

How would graduated trust scoring apply to the Devin case study? Would Devin have a trust path to a project maintainer? What friction level would it encounter at each hop distance?

Evidence: Graduated Trust proposal, Community Models in Seed, Devin Booking System

Next step: Map the Devin case study to Seed's community models — what would the trust path look like through formal communities vs. web of trust? Which model would Devin's contributions route through?

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What: Multiple agents writing to the same content-addressed document store is a novel pattern.

Open questions:

Do document-level leases work in practice, or do we need block-level leases?

How does content addressing simplify or complicate conflict detection?

What's the minimum viable coordination protocol for 2-5 agents?

Evidence: Product Backlog — Opportunity 7 — Multi-Agent Coordination Protocol.

Next step: Run a conflict experiment: two agents edit the same document simultaneously. Measure result. Design lease protocol.

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What: The LLM Wiki spec defines three self-healing mechanisms: version tracking (detect source changes, patch incrementally), deletion handling (remove dependent content when source is deleted), and periodic regeneration (full rebuild after N cycles to prevent drift). These form a reusable pattern language for LLM-maintained knowledge bases.

Open questions:

What is the optimal N for periodic regeneration? Too frequent = expensive, too infrequent = drift.

How do you detect and measure "knowledge drift" — the gradual divergence between wiki content and source documents?

Can the contradiction log serve as a drift metric? (More contradictions = more drift = regeneration needed sooner.)

Do the self-healing patterns generalize to other domains (code documentation, scientific literature review, legal research)?

The human-curated flag creates an append-only mode. Does this work in practice, or do humans need more granular override?

Evidence: LLM Wiki project document — self-healing section, human-override section, contradiction log section

Next step: Prototype a minimal wiki pipeline with one source document. Measure drift over 10 incremental LLM edits. Determine optimal regeneration frequency.

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What: State machines are great, but hard to establish documents adoption friction.

Hypothesis: The lack of good debugging/visualization tools is the primary barrier.

Next step: Survey XState community on adoption pain points.

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What: How do we measure whether an AI-generated contribution is genuinely useful vs. plausible but wrong?

Open questions:

Can we detect "slop PRs" by analyzing code patterns (test coverage, dead code, unnecessary abstraction)?

What signals distinguish a thoughtful AI contribution from a noisy one?

The Devin case study is a rare documented example of high-quality AI contributions (6 merged PRs). What patterns in the Devin workflow produce quality? Can those patterns be formalized into metrics?

Evidence: Graduated Trust proposal, Devin Booking System

Next step: Collect 10 real slop PRs from open source. Analyze patterns against the Devin case study patterns. Propose detection heuristics.

What: The Community Models in Seed document defines two trust models, but doesn't specify how they interact. Can a user be in both a formal community and a web of trust simultaneously? Which model takes priority for hop-distance calculation when both apply?

Open questions:

When a user is both a formal community member (hop-0) and a web-of-trust follower (hop-2 from a maintainer), which distance wins? Minimum? Average?

Can web-of-trust edges override formal community role distance? (Example: a maintainer follows someone outside the community — does that give them hop-1 access?)

What happens when formal community permissions conflict with web-of-trust signals? Who decides precedence?

Evidence: Community Models in Seed — the two models defined; Graduated Trust — where they'd be applied

Next step: Model the interaction between the two trust models. Propose a precedence rule: formal community membership wins for permission gating, web of trust for discovery/relevance. Test against a hypothetical Seed space with 3 community members and 10 followers.