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Aditya Karnam
AI researcher building the infrastructure layer for reliable agents.
Now

Current experiments and active fronts.

This page is a working board for what matters right now in the lab. It stays grounded in the artifacts already published here instead of turning into a vague status page.

Current focus
making local agent runtimes observable
turning retrieval into durable memory infrastructure
mapping the memory ecosystem with MCP in view
keeping human-facing tooling explicit and inspectable
Active Board

What is in motion

The active board emphasizes specific published work and the concrete system questions it is pushing on.

Local agent runtime control

ActiveAI agent infrastructure

The freshest published signal is subagent-fleet, dated July 1, 2026. The work centers on role-aware routing, local node health, warmup, and observability for Claude Code-style subagents.

Retrieval becoming memory infrastructure

ShippingAI agent infrastructure

embenx is the clearest retrieval-layer artifact: one API across many backends, with hybrid search, temporal memory, reranking hooks, and MCP-native long-term memory support.

Memory landscape mapping

OngoingAI agent infrastructure

awesome-agentic-memory is the ecosystem map. It keeps the memory layer legible by comparing frameworks, MCP servers, and backend patterns instead of treating memory as one product feature.

Operator tooling and prompt surfaces

LiveAI agent infrastructure

AI Toolkit remains a practical sandbox for prompt composition, grading, rewriting, and output shaping. It is smaller than the other systems but useful as an interface-design proving ground.

Reading Path

If you want to understand the current wedge

This is the shortest path through the existing work if you want the thesis before the broader site catches up.

Start with subagent-fleet for runtime orchestration and local model routing.
Read embenx next for retrieval, temporal memory, and MCP-native long-term context.
Use awesome-agentic-memory to place both projects inside the wider memory landscape.
Finish with AI Toolkit to see the smaller operator-facing interfaces that inform the bigger systems.

Planned field notes

These are still planned, not published. They are listed here to show where the writing likely expands next.

planned field note: Missing infrastructure layer for reliable AI agents
planned field note: From RAG to state: why agent memory is not just retrieval
planned field note: Local-first AI infrastructure for agent builders
© 2026 Aditya Karnam. AI Researcher.
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