← All selected work
AI systems architecture
State-aware AI orchestration across enterprise data
A conversational architecture coordinating memory, attachments, retrieval, specialist tools, and synthesis.
Primary evidenceMulti-agent
production-oriented workflow
Situation
What the system needed to change
Enterprise users needed to query structured tables and uploaded files through one conversational surface without collapsing every responsibility into one prompt.
Constraints
The boundaries mattered.
- 01
Context and memory had to remain explicit and inspectable
- 02
Retrieval required metadata-aware filtering and deduplication
- 03
Binary attachments and tabular data needed different processing paths
Decisions
The important engineering choices
- Separated orchestration, specialist knowledge tools, and final synthesis
- Added metadata injection, dynamic filters, and hash-based retrieval controls
- Designed natural-language access across Databricks tables and uploaded CSV or Excel files
- Moved suitable orchestration from Databricks to n8n to reduce operating complexity
Outcome
What the work left behind
- An executable multi-agent workflow rather than an architecture-only exercise
- Clearer boundaries for context, retrieval, progress, and final response generation
- A path to lower-cost and more maintainable orchestration