Applied AI - Automation - Reliable agent systems
Building AI systems that can be inspected, governed and used in practice
I combine applied AI with more than 20 years in enterprise IT, automation, operations and cybersecurity. My work covers retrieval systems, agent orchestration, user-facing AI products, provenance, evaluation and controlled execution.
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Two ways to review the work
The proof packages show implemented systems. The methods collection shows the control models, reasoning structures and verification practices behind them.
Recruiter route
Proof packages
Review current evidence for retrieval, agent safety, product engineering, provenance and runtime governance.
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Method route
Methods, protocols and prompt systems
Explore interactive profiles for 5PP, dialogue lifecycle, ORBIT, AICS, DIALECTIC, RigVedan and the DIAL-4 family.
Open the methods collection ->
Recruiter proof packages
Four current flagships
Each package separates implementation evidence, public demonstration, source pins, limitations and non-claims.
Retrieval and graphs
Intelligence Engine
Hybrid lexical, semantic and graph retrieval with schema-driven domains, REST, MCP and an interactive web UI.
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Human-in-the-loop agents
MADS
A controlled development environment where agent proposals become reviewable ChangeSets rather than uncontrolled host actions.
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AI product engineering
Adaptivearts.ai
Astro, React and Supabase product architecture with editorial workflows and a server-side AI provider boundary.
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Runtime governance
Gate Monitor
Deterministic policy for long-running agent sessions using evidence, thresholds, findings and explicit decisions.
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Methods and protocols
The systems behind the systems
Interactive profiles explain how work is clarified, scoped, authorized, represented, verified and kept honest about uncertainty.
Browse all method profilesEarlier showcases and experiments
Broader project archive
These examples remain available as historical and supporting demonstrations. Current source-pinned proof packages are listed above.
8me Orchestration
Progressive loop-orchestration labs and persistent AI workflows.
SPINE Showcase
Multi-agent orchestration, context engineering and execution surfaces.
Adaptive MCP Orchestrator
Capability routing, fallback and observability reference implementation.
arbiter
MCP protocol validation, quality checks and remediation packs.
Security Audit MCP
Security scanning and isolated analysis patterns.
agentspool
Inter-agent messaging and delivery semantics.
switchcore
MCP discovery, inventory and bounded workflow recommendations.
vigil
Durable follow-up checks with retry and expiry.
spawn
Pattern-driven MCP project generation.
Music Video Creator
Beat-aware rendering and multimodal output experiments.
AI-Human Admin Dashboard
Human and machine-readable project coordination views.
From Blueprint to Application
Book and interactive demonstrations for structured AI delivery.
How I work
Evidence before confidence
I prefer systems where implementation claims can be inspected and risky actions remain bounded.
- - Pin source and implementation receipts.
- - Separate tests, runtime evidence and documentation claims.
- - Replace private operational data with synthetic public demonstrations.
- - Keep limitations and non-claims beside strengths.
- - Route risky actions through policy or human approval.
- - Preserve earlier evidence when systems evolve.