DOCS: add logo and update blurbs

Author: mdw771

README.md

@@ -1,8 +1,27 @@
 # Experiment Automation Agents (EAA)
 
-EAA is a Python toolkit for building experiment-facing agents around shared
-task-manager, tool, memory, skill, and WebUI primitives. The repository is a
-`uv` workspace with three installable packages:
+![logo](docs/assets/eaa_logo.png)
+
+EAA is a runtime and harness for agents and workflows, designed for running scientific experiments especially for those needing vision. EAA turns a multimodal LLM into an experiment operator with features including the following:
+- A built-in LLM chat loop
+- An image-followup mechanism allowing LLMs to "see" images yielded by tools, even if the LLM provider's API doesn't allow images in tool responses
+- Automatic image captioning
+- Support of MCP servers and external HTTP endpoints as tools
+- Support of agent skills
+- Built-in tools including
+  - File read/write
+  - Python/bash coding (including Bubblewrap sandboxing)
+  - Local environment setup with `uv`
+  - Image rendering tool allowing LLM to see floating point TIFFs
+  - Subagent and sub-task manager spawning
+- Fully customizable "task manager" data structure
+- A WebUI for easy interaction and visualization of sessions
+
+**EAA is not only for LLM agents!** Fully logic-driven or rule-based workflows - such as Bayesian optimization - can be packaged as task managers. Use MCP servers as RPC calls through the built-in MCP-RPC wrapper, and display status and progress using the same WebUI. Logic-driven task managers can be added to the sub-task manager tool so that an LLM agent can launch them.
+
+Read (or let your agent read) the full documentation [here](https://AdvancedPhotonSource.github.io/EAA).
+
+This repository is a `uv` workspace with three installable packages:
 
 - `eaa-core`: shared runtime, generic tools, memory, WebUI, MCP helpers, and
   reusable task-manager infrastructure

docs/assets/eaa_logo.png

@@ -1,8 +1,23 @@
 # Experiment Automation Agents
 
-EAA provides a shared runtime for experiment-facing agents built from task
-managers, tools, optional long-term memory, and a lightweight standalone WebUI.
-This documentation tracks the current codebase rather than a future roadmap.
+![logo](assets/eaa_logo.png)
+
+EAA is a runtime and harness for agents and workflows, designed for running scientific experiments especially for those needing vision. EAA turns a multimodal LLM into an experiment operator with features including the following:
+- A built-in LLM chat loop
+- An image-followup mechanism allowing LLMs to "see" images yielded by tools, even if the LLM provider's API doesn't allow images in tool responses
+- Automatic image captioning
+- Support of MCP servers and external HTTP endpoints as tools
+- Support of agent skills
+- Built-in tools including
+  - File read/write
+  - Python/bash coding (including Bubblewrap sandboxing)
+  - Local environment setup with `uv`
+  - Image rendering tool allowing LLM to see floating point TIFFs
+  - Subagent and sub-task manager spawning
+- Fully customizable "task manager" data structure
+- A WebUI for easy interaction and visualization of sessions
+
+**EAA is not only for LLM agents!** Fully logic-driven or rule-based workflows - such as Bayesian optimization - can be packaged as task managers. Use MCP servers as RPC calls through the built-in MCP-RPC wrapper, and display status and progress using the same WebUI. Logic-driven task managers can be added to the sub-task manager tool so that an LLM agent can launch them.
 
 Start with the [quick start](quick-start.md), or use the [guide](guide.md) for
 installation, configuration, tools, skills, memory, and WebUI usage.