Architecting the AI-Native Enterprise
Context as Infrastructure: Memory, Data Pipelines, and Production Agentics
Building production-ready AI isn't just about the model, it’s about the context control plane. In the enterprise, the challenge has shifted from simple prompt engineering to the complex orchestration of state, memory, and high-fidelity data streams.
Join us for a technical deep dive into the foundations of the AI-native stack. We’re bringing together the builders solving the "Memory Trilemma", balancing accuracy, latency, and cost at the infra level.
The Panel [Name], [Company]: Building [e.g., Real-time Context Orchestration Layers]
[Name], [Company]: Architecting [e.g., Distributed Vector Memory & State Engines]
[Name], [Company]: Engineering [e.g., Agent-to-Agent Identity & Governance Protocols]
What to Expect Architectural Tradeoffs: No fluff. Pure discussion on the practical constraints of managing massive context windows and the hardware/software bottlenecks of 2026.
High-Signal Networking: Designed for Principal Engineers, Infra Leads, and Data Architects.
What to Expect?
Context Engineering vs. Prompt Engineering
We'll discuss the transition to Contextual Grounding, architecting systems that dynamically curate organizational and task-specific context before a single token is generated.
The Memory Wall: Solving for state management in long-running agentic loops. A look at persistent KV cache management, semantic memory tiers, and the "Agentic Identity" frameworks required for secure, multi-step reasoning.
Data Pipelines for Reasoning
Integrating unstructured enterprise data into active decision engines. How to move from batch ETL to real-time context-graph abstractions that agents can navigate autonomously.
Engineering the Loop (Observe → Reason → Act)
Managing the reliability and observability of non-deterministic workflows. Discussion on checkpointing, durability, and loop-detection in production-scale agentic clusters.
Evolution of progress
Context engineering across touchpoints is now essential. Great products anticipate user needs and guide them toward solutions before they've even articulated the problem
AI-Native Growth
Building AI-Native companies means embedding AI principles into both the codebase and the user experience. Efforts to shape how people naturally interact with your product are incredibly important
Panelists
Kevin Gu - ThirdLayer, Founder and CTO
Speaker - Company, Position
Speaker - Company, Position
Speaker - Company, Position
Speaker - Company, Position
Partners
Novita AI -