Add agent-supervisor example

[shahryarkhalid-cmd]

Fleshes out the agent_supervisor.py template in examples/templates/ into a
full, runnable example (examples/agent-supervisor/). Ports LangGraph's
"agent supervisor" pattern to Burr: an LLM-driven supervisor that routes
between a researcher agent (web search) and a coder agent (python
execution / charting), deciding the next step at each turn rather than using
fixed transitions.

Scope was discussed beforehand with @Stefan K on Discord, who suggested this
be built as a standalone example that implements the existing template,
rather than modifying templates/ directly.

Changes

• New examples/agent-supervisor/ folder, following the standard example
  structure:
  • application.py -- supervisor_agent, researcher, coder, tool_node,
    and terminal actions, wired up with ApplicationBuilder. Uses the plain
    OpenAI SDK for LLM calls (matching examples/email-assistant's style)
    rather than LangChain or Hamilton, to keep the example dependency-light.
  • README.md -- explains the example and how it relates to the template
    and to examples/multi-agent-collaboration.
  • requirements.txt, notebook.ipynb, __init__.py, statemachine.png
    (generated via app.visualize()).

This differs from examples/multi-agent-collaboration (which covers the same
researcher/coder domain) in that routing between agents is decided by the
supervisor's own LLM call at each step, rather than fixed transitions --
demonstrating the supervisor pattern specifically, as the template intends.

How I tested this

• Verified the Burr application graph builds correctly, with all actions and
  transitions wired as expected.
• Verified app.visualize() produces the expected state diagram (included as
  statemachine.png).
• Ran the full application end-to-end against Groq's OpenAI-compatible
  endpoint (as a stand-in, since I didn't have an OpenAI key on hand) to
  validate the supervisor's routing logic, the tool-calling loop, and
  termination behavior all work correctly.
• Have not yet run it against the actual OpenAI API the shipped code targets
  -- happy to do that and confirm, or if a reviewer can sanity-check with
  their own key that'd also be appreciated.

Notes

• Open to feedback on the researcher/coder domain choice, or on the routing
  prompt in supervisor_agent if it should be tuned further.
• TAVILY_API_KEY is optional -- without it, web_search returns a
  placeholder string so the example still runs.

Checklist

• PR has an informative and human-readable title
• Changes are limited to a single goal (new example only, no unrelated changes)
• Code passed the pre-commit check & code is left cleaner/nicer than when first encountered.
• Any change in functionality is tested
• New functions are documented (with a description, list of inputs, and expected output)
• Placeholder code is flagged / future TODOs are captured in comments
• Project documentation has been updated if adding/changing functionality.