report(1H): add Agentic Workloads (§6) and HW-Spec Search (§7) sections

- §6 Supporting Agentic Workloads: how the fused GQA design extends to
  agentic fan-out/fan-in; three-layer split (framework/runtime/kernel);
  Stationary-KV vs Distributed-Q execution policies.
- §7 Hardware Performance-Spec Search for GQA: WIP stub (sweep intent
  over GEMM TFLOPS, MATH-engine ALUs, CUBE↔CUBE and SIP↔SIP BW).
- Renumber Discussion/Conclusion/Future-Work to 08/09/10; update
  main.tex input order and toc.md.
- Add Agentic_Runtime_Architecture.md design note; rebuild main.pdf.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/report/1H-codesign-paper/sections/10-future-work.tex

@@ -0,0 +1,51 @@
+\section{Future Work}
+\label{sec:future}
+
+The 1H work covered attention end to end. The natural next step is to
+complete the decoder and then to ask how compute and data should be
+distributed for realistic, agentic workloads.
+
+\paragraph{From attention to the full decoder: FFN and MoE.} A decoder
+block is attention followed by a feed-forward network (FFN), and in modern
+models that FFN is increasingly a mixture-of-experts (MoE) layer. The FFN
+is the compute-rich counterpart to attention---it is where the composite
+command's MAC-efficiency gains (\S\ref{sec:gemm}) should matter most---so
+adding it gives a balanced view of a full block instead of its
+memory-bound half alone. MoE adds a new dimension: a routing step selects a
+few experts per token, turning the dense FFN GEMM into a sparse, data-
+dependent dispatch. The open questions are how to issue expert GEMMs as
+composites under data-dependent token counts, and how to move tokens to
+experts efficiently---an all-to-all-shaped communication pattern distinct
+from the all-reduce studied here, and a natural extension of the PE\_IPCQ
+work.
+
+\paragraph{Compute and data distribution for full LLM decoding.} With both
+attention and FFN/MoE in hand, the question becomes where each layer's
+compute and state should live. Attention is KV-bound and favors keeping the
+KV cache close to the PEs that consume it; FFN/MoE is compute-bound and
+favors spreading GEMM work across PEs; MoE routing makes the optimal
+placement token- and time-dependent. A 2H goal is to use KernBench to
+explore these placement and parallelization trade-offs---tensor vs.\ expert
+vs.\ sequence parallelism---under a single, software-stack-independent
+model, so the interconnect and memory implications of each choice are
+measured rather than assumed.
+
+\paragraph{Agentic workloads: from design to implementation.}
+Section~\ref{sec:agentic} established \emph{how} the fused GQA design
+extends to agentic fan-out/fan-in and \emph{which} responsibilities fall to
+the framework, the runtime, and the kernel. The 2H step is no longer to
+analyse the workload but to \emph{build} that support: the detailed design
+and implementation of all three levels. This is necessary work rather than
+optional, because today's agentic frameworks are effectively all
+closed-source---there is no open substrate that exposes shared-prefix KV
+reuse, cross-agent query batching, and schema/pointer-based fan-in down to
+the hardware. Concretely, 2H targets: a \textbf{framework} layer that
+manages the agent tree, identifies co-schedulable branches, and enforces
+compact fan-in; a \textbf{runtime} layer that forks logical contexts by
+page table without copying shared KV, batches sub-agent query rows, and
+selects the execution policy; and a \textbf{kernel} layer that runs the
+batched replicated-Q attention and hierarchical online-softmax merge over
+PE\_IPCQ. Bringing these up on KernBench turns the
+Section~\ref{sec:agentic} design into a measured, end-to-end agentic path
+and lets us confirm which of the 1H hardware levers still dominate once the
+full framework/runtime/kernel stack is in place.