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I will design advanced ai agent architecture and custom rag memory systems


About this gig
I am a specialized AI Architect specializing in advanced Agent Memory Systems and Knowledge Graph Networks. I design the logical blueprints and data flow schemas required to build bulletproof, state-aware AI applications.
What I Will Design For You:
Multi-Agent Committee Routing: Decouple task execution from state maintenance using custom agent validation loops (Critic/Archivist/Operator models) to eliminate cognitive drift.
Advanced Knowledge & Concept Maps: Transition your system from static document indexing to a dynamically evolving 5-layer system taxonomy.
Memory Trackers: Blueprints for PagedAttention frameworks, Git-Memory arrays, and Temporal Token Lineage Tracking (TTLT) to maintain clean state history trees.
Context & Budget Optimization: Implement precise Observation Masking schemas to filter out background tool-call noise and drop operational token overhead by up to 90%. What You Get: A comprehensive, professional technical system architecture document (Markdown/PDF format). Beautiful, pristine system topology and data lineage flow diagrams ready for your developers. Precise object schemas (JSON/JSON-LD) for your graph database setup. Please send over a brief summary
Get to know Ananthakrishnan
- FromIndia
- Member sinceOct 2024
Languages
Malayalam, English, Hindi
FAQ
Do you write the deployment code or build the database in this gig?
No, this is an architectural and conceptual design gig. I create the comprehensive system blueprints, multi-agent logic flows, and data schemas. This ensures your development team avoids costly state-drift mistakes before writing a single line of code.
What frameworks do your architectural designs support?
My blueprints are framework-agnostic but are optimized for smooth deployment across LangChain, LlamaIndex, CrewAI, AutoGen, and MemGPT-style operating systems.
How do your designs reduce token costs by 90%?
By incorporating strict Observation Masking filters into the workflow topology. This strips away repetitive system execution data and raw tool-call noise before the clean data stream hits the persistent memory layer.

