Positioning scan
Confirm the headline role, strongest proof answer, and exact claim boundary before reading the broader archive.
Machine Intelligence Engineer / Software Architect
Persistent AI memory with clear boundaries between local continuity and runtime coordination.
Michael Kappel’s AI-memory work separates two problems that are often blurred together: UAIX/.uai provides typed local startup and handoff continuity for a repository or project, while Multi-Agent Memory provides a shared runtime layer designed for registered external agents. One preserves context across sessions; the other adds coordination and shared services.
How to review Michael
Choose the right depth before opening the archive.
Separate local .uai handoff continuity from shared runtime coordination before evaluating persistent memory claims.
Recruiter path
Use the concise resume, current contact path, and role-level experience timeline to qualify fit quickly.
Hiring-manager path
Move from role fit into selected projects and case studies, then inspect the Evidence Map if a claim needs traceability.
Technical architecture path
Inspect Multi-Agent Memory, persistent AI memory, local LLM infrastructure, package evidence, validation, and non-claims.
Machine-readable review path
Use this path for AI agents, answer engines, ATS tools, and structured review without treating hidden files as public claims.
Machine-intelligence claim path
Answer the core proof question directly, then branch into multi-agent systems, AI memory, LLM infrastructure, and Intelligence724 boundaries.
Azure / .NET / SQL path
Confirm the enterprise engineering foundation with role-level Azure usage, .NET/SQL modernization, and conservative evidence boundaries.
Claim to proof matrix
What each major claim proves - and what it does not prove.
This matrix keeps SEO, AEO, GEO, recruiter review, and technical review aligned to the same conservative evidence boundaries.
Machine Intelligence Engineer / Software Architect
Proof: Machine Intelligence overview, proof brief, resume, case studies, and Evidence Map.
Boundary: Does not claim foundation-model training, novel ML research, benchmark leadership, or autonomous systems without human governance.
Multi-agent systems and coordination architecture
Proof: Multi-Agent Systems page, Multi-Agent Memory case study, public repository, companion docs, and evidence brief.
Boundary: Does not claim hosted SaaS, OSI-approved open source, production scale, distributed consensus, model hosting, or automatic conflict-free merging.
Persistent AI memory and handoff
Proof: AI Memory page, AI memory handoff route, persistent memory evidence brief, and .uai handoff records.
Boundary: Does not claim automatic sync, automatic memory promotion, private repository writes, credential access, or runtime orchestration authority.
LLM infrastructure and local runtime tooling
Proof: LLM Infrastructure page, NuGet package profile, package map, and LLM infrastructure evidence brief.
Boundary: Does not claim foundation-model training, public GPU inference benchmarks, or unverified production inference scale.
Azure / .NET / SQL enterprise architecture
Proof: Experience timeline, resume, .NET/SQL modernization route, and Azure evidence brief.
Boundary: Does not infer unconfirmed scale, private infrastructure, certifications, client names, or business outcomes.
Code intelligence and AI-assisted modernization
Proof: Experience page, Intelligence724 public capability surface, and code-intelligence boundary brief.
Boundary: Does not disclose confidential Info724/client systems or claim that Intelligence724 and Info724 are the same legal entity.
Research reports as supporting context
Proof: Reports archive, research search, docs manifest, and curated Architecture Notes promoted after review.
Boundary: Raw reports are not resume claims; sensitive/search-only records stay out of navigation and sitemaps unless searched for.
Fast recruiter summary
Two memory lanes, two different responsibilities.
The separation prevents a local handoff package from being mistaken for a live multi-agent platform.
Lane 1 / UAIX and .uai
Typed local startup and handoff continuity.
Repository-local files preserve identity, purpose, constraints, active state, source authority, coding expectations, recovery notes, and next-step handoff information for a future session or agent.
Lane 2 / Multi-Agent Memory
Shared runtime memory and coordination.
A deployable private-intranet reference system adds registered identities, organizational scopes, claims, leases, message routing, acknowledgements, durable records, search, synchronization, and human verification.
Engineering value
Continuity without hidden authority.
Both lanes keep source status, scope, lifecycle, and review visible so remembered context can assist work without silently becoming unquestioned truth.
Local continuity diagram
How a .uai package carries project state across sessions.
This is a file-based startup and handoff flow. It is deliberately not presented as a message bus or orchestrator.
1. Startup indexA compact entry point identifies the reading order and authoritative project sources.
2. Identity and purposeRole, project intent, positive anchors, and hard boundaries define the local operating context.
3. Active memoryShort-term state records current facts, decisions, open work, and evidence still awaiting review.
4. Engineering constraintsCoding standards, source authority, test requirements, and safety rules constrain implementation.
5. File and project handoffChanged surfaces, recovery details, next actions, and validation state support a clean continuation.
6. Next sessionA future human or agent rehydrates bounded context without depending on the original chat transcript.
Memory design principles
Durability is useful only when provenance and review survive with it.
The architecture treats memory as governed project state, not a free-form accumulation of model output.
Typed responsibilities
Identity, world context, constraints, short-term state, file handoff, project handoff, and long-term pointers live in distinct records with distinct jobs.
Source authority
The memory points back to code, approved documents, tests, registries, and human decisions rather than replacing those sources.
Lifecycle awareness
Current, proposed, reviewed, sensitive, superseded, and archival material can be handled differently instead of flattened into one retrieval pool.
Compact startup
A deliberate reading order restores the smallest useful working set first, then routes deeper only when a task requires it.
Recovery orientation
Handoff records capture what changed, what was verified, what remains open, and how a future operator can resume safely.
Human governance
Memory can inform work, but publication, operational changes, stronger claims, and sensitive decisions retain explicit review gates.
Evidence path
Inspect local continuity and shared runtime memory separately.
Each link answers a different reviewer question.
How is local context organized?
AI knowledge architecture handoff explains the local package, reading order, approval boundary, and public/private separation.
What is the public memory manifest?
uai-memory-manifest.json provides a machine-readable public view of active handoff files and their roles.
What adds live coordination?
Multi-Agent Memory supplies the source-available shared runtime reference implementation.
Explicit non-claims
A .uai package does not itself orchestrate or message agents.
UAIX/.uai is typed local startup and handoff continuity. By itself it is not a scheduler, live message broker, shared database, execution engine, or proof that multiple agents are concurrently active. It also does not make remembered content true: sources, lifecycle labels, tests, and human review remain authoritative. Runtime coordination claims belong to the separately evidenced Multi-Agent Memory implementation.
FAQ
Common review questions.
Short answers are rendered in the HTML source and mirrored into JSON-LD where appropriate.
How do .uai files and Multi-Agent Memory differ?
Typed .uai files preserve local startup, identity, constraints, handoff, and recovery context. Multi-Agent Memory adds authenticated, scoped, reviewed shared memory and coordination for external agents.
Does .uai provide runtime orchestration?
No. .uai is a portable continuity and handoff model; it does not provide hosted messaging, automatic synchronization, repository writes, or agent certification.
How is memory governed?
Memory remains source-bounded and review-aware, with explicit status, scope, promotion, rejection, quarantine, readback, and human verification paths.