diff --git "a/2026/agentic-ai-aws/presentation.html" "b/2026/agentic-ai-aws/presentation.html" new file mode 100644--- /dev/null +++ "b/2026/agentic-ai-aws/presentation.html" @@ -0,0 +1,565 @@ +Shaun Smith `@evalstate`
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Hugging FaceGitHubgithub.com/evalstate
+
Agentic AI on CPU · London · 2026
+
Upskilling Local Models and Agents
+
Shaun Smith · Adrian Lepers
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May 2026
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Hugging FaceGitHubgithub.com/evalstate
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Shaun Smith @evalstate

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  • Open Source @ Hugging Face
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  • MCP Maintainer [Transports] / Community Moderator
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  • huggingface/hf-mcp-server
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  • huggingface/upskill
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  • huggingface/skills
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  • Maintainer of fast-agent
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+ Hugging Face +
+ MCP +
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Hugging FaceGitHubgithub.com/evalstate
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Hugging FaceGitHubgithub.com/evalstate
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The evolution of Tool Calling and Model Inference....

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Hugging FaceGitHubgithub.com/evalstate
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Things we didn't have 18 Months ago...

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Model Context Protocol

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AGENTS.MD and Agent Skills

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MoE Models and Efficient Quantizations

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Agent Client Protocol

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Long Running Tool Loops (and reasoning models)

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Hugging FaceGitHubgithub.com/evalstate
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Reinforcement Learning

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Models are placed in an environment, given a task and scored with a reward function:

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  • discover
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  • self-correct
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  • problem solve
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  • keep driving the loop without constant human steering
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mini-SWE-Agent: A single 100 line python and single freeform (non JSON) tool can score 76.0% on SWE-Bench!

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It's hard to compete against that efficiency.

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alt text

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+ Reinforcement learning environment diagram + SWE-Bench bash tool benchmark result +
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+
Hugging FaceGitHubgithub.com/evalstate
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Smaller and Simpler Harnesses

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  • General-purpose agent harnesses are given direct(*) shell access
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  • Fewer pre/post tool and LLM stop checks to keep models on track
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  • API surface and Custom Workflows replaced by Model capabilities
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  • Snapshot and checkpointing techniques
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  • Movable runtime environments
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  • Scripting (code generation) allows immediate specialization
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+
Hugging FaceGitHubgithub.com/evalstate
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Self Directing Models

+Task flows to Navigate, Ingest, Act, then loops back to Task +
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+
Hugging FaceGitHubgithub.com/evalstate
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Dynamic Tool Calling

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Dynamic Space Tool: 45 tokens

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MCP provides an inference gateway to thousands of specialized and custom models covering Audio, Video, Text, 3D Models, Environments and more.

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MCP provides Authentication and Multimodal support.

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Qwen 3.5-35B-A3B
+Flux.1-Krea-Dev
+Qwen-Edit-2509-Multiple-angles-LoRA
+Wan2.2 First/Last Frame

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Hugging FaceGitHubgithub.com/evalstate
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Why This Enabled Skills

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  • Simple to navigate native content hierarchy
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  • Unsurprising Token Dense format (bash!)
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  • Reusable procedures become scaffolding for capable models
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  • Script access requires fewer mid-context tool tricks
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  • Between deterministic program and documentation
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+
Hugging FaceGitHubgithub.com/evalstate
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Training Models

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+LLM Trainer Skill +

Fine-tune Qwen3-0.6B on the dataset open-r1/codeforces-cots

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Handles:

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  • Dataset Construction
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  • Dataset Selection and Validation
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  • Hardware Selection
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  • Training Scripts
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  • Job Submission and Monitoring
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  • Trackio Supervision
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  • GGUF Conversion
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Recently added Vision Training!

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Hugging FaceGitHubgithub.com/evalstate
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https://github.com/huggingface/upskill

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Hugging FaceGitHubgithub.com/evalstate
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Upskill

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  • Run in Sandboxes, View Traces, Optimise and Benchmark
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+Upskill trace chart +
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+Upskill benchmark output +
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  • Tutor and Select best Price/Performance Models
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Hugging FaceGitHubgithub.com/evalstate
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Code Execution Tools

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A model with access to general purposes tools has crossed into a very real form of code mode.

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Bash provides a general purpose, token dense-execution language.

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Task-specific tools generated on demand. Example: HF Tool Builder navigates OpenAPI spec to build composable CLI tools.

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Some models are trained to use code tools natively, and are bundled with interpreters.

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Hugging FaceGitHubgithub.com/evalstate
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Local model capability is compounding
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+ Between May 2024 and May 2026, the most expensive MacBook Pro you could buy stayed at + 128 GB of unified memory. The hardware ceiling barely moved. But the smartest + open-weight model you could actually run on it went from a score of 10 + — Llama 3 70B — to 47 — DeepSeek V4 Flash on antirez's mixed-Q2 GGUF — + on the Artificial Analysis Intelligence Index. +
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+ Artificial Analysis local model intelligence chart +
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+ That is 4.7× in 24 months + or a doubling of intelligence every 10.7 months +
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+ Source: “Local Moore’s Law” by Mishig Davaadorj · + huggingface.co/blog/mishig/local-moores-law +
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Hugging FaceGitHubgithub.com/evalstate
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Hugging FaceGitHubgithub.com/evalstate
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+

OpenAI Privacy Filter

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+ Privacy filter screenshot +
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+ +
can you help me.
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my name is "shaun smith"

+my credit card is "4929 1003 4422 4042"

+the API key I have been using is

+"sk-proj-rr3399393922220202".

+
You can reach me at shaun.smith@private-email.com

+or +44 7700 900123.

+
My home address is 221B Baker Street,

+London NW1 6XE.

+
My AWS access key is AKIAIOSFODNN7EXAMPLE

+and the secret is

+wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY.

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My national insurance number is QQ 12 34 56 C.

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+
Hugging FaceGitHubgithub.com/evalstate
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Some thoughts

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  • Owning and usefully customising and improving your own models is accessible
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  • Frontier Models are overused: Price/Performance
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  • Inference and Execution environments are blending
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  • Self Improvement is here if you want it
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Hugging FaceGitHubgithub.com/evalstate
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The takeaway
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+ Open source doesn't mean + ungoverned. +
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Own the weights.
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Set the policy.
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Don’t only trust someone else’s black box.
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+ Hugging Face + Hugging Face +
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+
Hugging FaceGitHubgithub.com/evalstate
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Thank You!

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+ GitHub + github.com/evalstate +
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+ X + x.com/evalstate +
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Hugging FaceGitHubgithub.com/evalstate
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Agent Client Protocol

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+ File and Shell Tools +

Client provided tools, enables "follow along" in editors

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+ Session Based +

Listing, Resumption and Rehydration of Agent sessions

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+ Streaming Results and Observability +

Agent Results and Tool Status stream, are cancellable

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+ MCP Native Support +

Uses MCP Data Model. Client sends MCP Sever Configurations

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Hugging FaceGitHubgithub.com/evalstate
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Open Responses

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Open standard extending OpenAI's Responses API. Provides a consistent, provider neutral way to interact with modern LLMs. Repairs Chat Completion API drift.

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It defines a shared schema, and tooling layer that enable a unified experience for calling language models, streaming results, and composing agentic workflows—independent of provider.

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Usage as a Provider / Router allows creation of rich Agent Environments

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+ Internal Tools - (Model or Provider) +
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  • shell and local_shell
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  • code_interpreter
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  • apply_patch
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  • web_search
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  • etc..
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+ External Tools (Client Supplied) +
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  • MCP Servers
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  • Standard JSON function calls
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  • Free-Form Tools
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  • Grammar constrained Tools
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Hugging FaceGitHubgithub.com/evalstate
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It was close....! PMF for MCP

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+ MCP is a Commodity Standard +

Supports Consumer, Enterprise and Developer use-cases.

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Single URL to install authenticated JSON tools across thousands of clients

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MCP's "fit" features weren't present at launch!

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URI/Resources based extensions deliver innovation and extensibility...

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...Which enabled rapid MCP Apps distribution on a solid support base.

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+ Model/Host Changes and STDIO +

Host applications with Shell tool reduce the need for STDIO Servers.

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In many cases for local running tools such as Apify mcp-cli or Pete Steinberger's MCPorter offer a better experience for MCP usage.

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Distribution via MCPB is one potential advantage

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Simple one-shot server design meant that distribution of ideas was more important than code.

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Hugging FaceGitHubgithub.com/evalstate
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Generation and Execution Environments

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Style 1 - Main Model owns Code Generation

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Main model
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Generates Search Function
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Execution Tool
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Uses Search Function to return API definitions
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Main model
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Generates code from that API surface
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Execution tool
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Runs the code and returns output
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Main model
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Reads result and writes final answer
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Code Generation: Main Model
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Code Execution: Tool Environment
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Style 2 - Delegated Code Generation

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Main model
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Sends a natural-language task to the tool
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Execution tool
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System Prompt contains API definitions
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Execution tool
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Returns the result
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Main model
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Packages it as the final answer
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Code Generation: Tool Model
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Code Generation: Tool Environment
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API Definitions Cacheable
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MCP makes it easy to transfer generation and execution between models and environments!
(and who pays for inference)

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Hugging FaceGitHubgithub.com/evalstate
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LLMs for Navigating: GenUI, Apps SDK (Prefect Prefab)

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A common pattern:

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  1. user asks for navigation or retrieval
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  2. tools fetch the answer
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  3. the model then spends expensive output tokens reprocessing a result that was already good enough
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  4. The MCP Apps pattern fixes this by letting the result become final for the user.
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Hugging FaceGitHubgithub.com/evalstate
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