// 01
LLM training
Pretraining data, scaling laws, synthetic data, RL from execution feedback, distillation and post-training systems built for production models.
Program // 08 parallel tracks
AI management from training to production. Agent engineering from loop to experience.
Open weight models vs Frontier Model : defining the future of API
The next decade of AI infrastructure will be shaped by a single architectural bet: will applications call closed frontier APIs, or will they run open-weight models behind their own endpoints? GenerationAI 2026 connects that decision to the complete operating lifecycle—from training and post-training through inference, agent harnesses, production loops and the experience people ultimately trust.
// 02
Agent experience
Designing the human surface of autonomous work: approvals, interruption, legibility, memory, trust calibration and collaborative control.
// 03
Harness engineering
Tool design, sandboxing, permissions, context assembly, observability and the execution layer that turns a model into a reliable system.
// 04
Loop engineering
Control flow for non-deterministic workers: planning, retries, reflection, context compaction, recovery and long-running agent loops.
// 05
Open weights vs frontier
What open-weight releases can now do, where closed frontier models still lead, and how that choice defines the future of the API.
// 06
Benchmarks & evals
Contamination, saturation, agentic task suites and internal evaluations that remain useful through model and harness changes.
// 07
Inference systems
Serving economics, KV cache strategy, speculative decoding and latency budgets for multi-step, high-concurrency agent workloads.
// 08
MCP & interoperability
Model Context Protocol servers in production: capability negotiation, authentication and the emerging interoperable tool ecosystem.