Sleep with Enter key and wake up in production.⌨️
Software Architecture | Data Engineering | AI/ML | Fintech, Healthcare.
I enjoy taking photos and capturing small moments.📸
Location
Houston, TX
Education
Bachelor of Computer Science, Texas Tech University
Your breakdown of memory types into working, semantic, and episodic is ideal. The intentional design approach, rather than just appending history, aligns well with traditional logging patterns.
26ai's enterprise-grade security features, like RLS policies and auditable retrievals, are a game-changer for production systems. These are must-haves for ensuring data integrity and compliance.
I'd treat preferences as a separate memory type, though, given their unique access patterns and needs.
The framework provides a solid roadmap for transitioning from memory-curious to memory-aware agents. The Oracle AI DevHub examples are great resources for developers looking to implement these patterns.
Looking forward to the Sovereign Synapse series!
Ken has a long history around computers starting with early Commodore PETs and VIC-20s. He is a MongoDB Certified Developer and lives in Oregon with his wife and three children. You can find him mo...
I appreciate the focus on the 'intentional design' aspect. Appending history is easy; curating it is engineering.
You’re absolutely right about enterprise-grade features like RLS (Row Level Security). In a Sovereign Infrastructure, those aren't just 'features'—they are the bedrock of Developer Trust (DT). If an architect can't audit exactly who saw what and why, they won't put the system into production.
I also take your point on treating Preferences as their own distinct memory type. They have a different 'half-life' and access pattern than episodic memory. Separating them ensures that our gateways stay efficient and don't waste tokens on fuzzy logic where exactness is required.
The Oracle AI DevHub examples really do provide a solid roadmap for these patterns—glad you found them useful.
Sleep with Enter key and wake up in production.⌨️
Software Architecture | Data Engineering | AI/ML | Fintech, Healthcare.
I enjoy taking photos and capturing small moments.📸
Location
Houston, TX
Education
Bachelor of Computer Science, Texas Tech University
Thanks for your article.
Your breakdown of memory types into working, semantic, and episodic is ideal. The intentional design approach, rather than just appending history, aligns well with traditional logging patterns.
26ai's enterprise-grade security features, like RLS policies and auditable retrievals, are a game-changer for production systems. These are must-haves for ensuring data integrity and compliance.
I'd treat preferences as a separate memory type, though, given their unique access patterns and needs.
The framework provides a solid roadmap for transitioning from memory-curious to memory-aware agents. The Oracle AI DevHub examples are great resources for developers looking to implement these patterns.
Looking forward to the Sovereign Synapse series!
I appreciate the focus on the 'intentional design' aspect. Appending history is easy; curating it is engineering.
You’re absolutely right about enterprise-grade features like RLS (Row Level Security). In a Sovereign Infrastructure, those aren't just 'features'—they are the bedrock of Developer Trust (DT). If an architect can't audit exactly who saw what and why, they won't put the system into production.
I also take your point on treating Preferences as their own distinct memory type. They have a different 'half-life' and access pattern than episodic memory. Separating them ensures that our gateways stay efficient and don't waste tokens on fuzzy logic where exactness is required.
The Oracle AI DevHub examples really do provide a solid roadmap for these patterns—glad you found them useful.
Thanks for your reply! I really appreciate it.