Research and prototype repository for experimenting with:
- GitHub Copilot agent workflows
- Context optimization and hygiene
- Agent observability
- Token and cost reduction
- Orchestrator / sub-agent architecture
- Persistent working memory patterns
- Runtime telemetry analysis
This repository is experimental and research-oriented. The workflows, prompts and architecture patterns are still evolving.
.
├── README.md
├── docs/
│ └── index.html
├── scripts/
│ ├── README.md
│ ├── analyze-events.csx
│ ├── export-events.csx
│ └── lib/
│ ├── evens-core.csx
│ └── events-annotations.csx
└── .copilot/
└── agents/
├── brainstorm.agent.md
├── build.agent.md
├── plan.agent.md
├── sub-debugger.agent.md
├── sub-explorer.agent.md
├── sub-researcher.agent.md
├── sub-reviewer.agent.md
└── util-workflow-analyst.agent.md
Contains the original HTML presentation used during a talk.
The presentation covers topics such as:
- Usage-based pricing impact
- Context entropy
- Agent loop behavior
- Context compaction
- Context hygiene
- MCP/tooling optimization
- Orchestrator/sub-agent architecture
- Persistent working memory
- Token cost optimization
- Observability gaps in current runtimes
The presentation is self-contained and can be opened directly in any browser.
Contains telemetry parsing and session analysis scripts for GitHub Copilot CLI runtime sessions.
Current tooling includes:
- session event parsing
- telemetry extraction
- tool usage analysis
- token usage analysis
- model usage analysis
- sub-agent execution tracing
- timeline reconstruction
- warning/error extraction
- structured export for further analysis
The scripts are intended for experimentation, debugging and workflow optimization.
See scripts/README.md for additional details.
Contains experimental custom workflow configuration and agent definitions.
The workflow is based on an orchestrator/sub-agent architecture where:
- orchestrators focus on reasoning and coordination
- worker agents execute specialized tasks
- expensive models are isolated to high-level reasoning
- cheaper models are used for execution and exploration
- context pollution is minimized through task isolation
The setup is intentionally modular and intended for experimentation and iteration.
Large noisy contexts reduce model quality, increase latency and dramatically increase cost.
This repository experiments with techniques such as:
- disabling unnecessary MCP servers
- minimizing tool exposure
- narrow and precise prompts
- isolated workflows
- reduced context pollution
- compact tool outputs
Long-running sessions tend to accumulate:
- context entropy
- duplicated information
- degraded reasoning quality
- excessive compaction
- unnecessary token consumption
This workflow encourages smaller isolated sessions with explicit state transfer when needed.
The architecture separates:
High-level reasoning
Handled by orchestrator agents using stronger models.
Specialized execution
Handled by lightweight worker agents using cheaper/faster models.
This helps reduce:
- token usage
- context growth
- tool noise
- compaction frequency
while improving:
- observability
- modularity
- execution speed
- workflow control
The workflow experiments with explicit persistent memory files instead of relying entirely on opaque runtime memory systems.
Advantages include:
- human-readable state
- editable memory
- cross-session reuse
- better post-compaction recovery
- explicit knowledge transfer
Current agent runtimes expose very limited visibility into:
- agent loops
- tool calls
- sub-agent behavior
- context growth
- runtime decisions
The included tooling attempts to improve runtime observability through telemetry analysis and structured event extraction.
- This repository is not production-ready.
- The workflows are experimental.
- Some ideas may change significantly over time.
- The setup is intentionally research-oriented.
- The implementation is optimized for exploration and iteration rather than stability.
The primary goal of this repository is to explore:
- scalable agent workflows
- efficient runtime architectures
- token-efficient orchestration
- observability tooling
- practical engineering patterns for LLM agents
especially under usage-based pricing models.
This repository represents personal research and experimentation. It is not affiliated with or endorsed by GitHub, Microsoft or Anthropic.
This repository is licensed under the MIT License. See LICENSE for more details.