• Weekly Learning
Stress-testing the Ideas to Life system under real agentic load
This week focused on applying the Ideas to Life process to a running experiment, using real constraints to refine how decisions, tooling, and documentation interact.
Context: This week’s learning was derived primarily from work on the Runner Agentic Intelligence experiment.
Related experiment: Runner Agentic Intelligence
This week’s focus
Apply and refine the Ideas to Life process by running it against a real experiment (Runner Agentic Intelligence), using evidence and constraints to test whether the system holds up beyond small, contained artefacts.
What actually happened
- Restarted development of the Runner Agentic Intelligence experiment, building on an existing PoC rather than starting from scratch.
- Made an early architectural decision to prioritise LLM abstraction to manage cost and vendor lock-in.
- Evaluated and adopted LiteLLM to support multiple model providers behind a single interface.
- Identified and resolved schema and prompt misalignment issues affecting model integration and output quality.
- Deferred multimodal support to stabilise the core text-based flow first.
- Introduced a strict separation between AS_BUILT (current state) and DELTA_BACKLOG (remaining gaps).
- Shifted documentation updates to be evidence-driven, regenerating them from PRs, tests, and verified capabilities.
- Updated the RUNBOOK to encode an evidence-first, closed-loop execution process.
- Resolved multiple tooling and environment issues related to macOS sandboxing, shell configuration, and test execution.
- Implemented foundational hardening steps (upload validation, FIT/FIT.GZ parsing) using real personal data.
- Improved Insights and Weekly Plan quality by tightening prompt structure and output contracts.
- Explicitly paused further feature work after stabilising the core loop, recognising the need for synthesis.
Key trade-offs
- Choosing early abstraction reduced future lock-in risk but increased upfront architectural complexity.
- Exploring multiple deployment and integration paths built confidence but introduced some rework before convergence.
- Tightening prompt structure constrained flexibility but significantly improved output usefulness.
- Pausing new feature development slowed visible progress but protected system stability.
- Relying on real data increased realism at the cost of more friction during testing and debugging.
What changed in my thinking
- Real, complex experiments surface system weaknesses that small artefacts do not.
- Prompt structure and contracts are often more limiting than model capability.
- Documentation drift is best treated as a process failure, not a discipline issue.
- Evidence-first updates reduce cognitive load and improve trust in system state.
- Explicitly choosing not to build can be as important as deciding what to build next.
Key takeaways
- Use real experiments to stress-test process assumptions early.
- Separate current reality from planned work to reduce planning noise.
- Treat prompts and schemas as first-class design artefacts.
- Derive documentation from evidence to prevent drift.
- Protect stability by making deliberate pauses visible and explicit.
Looking ahead (optional)
- Make assumptions and constraints explicit earlier in new experiments.
- Add lightweight safeguards around destructive local operations.
- Introduce a formal step to capture invalidated assumptions as part of the weekly cycle.
Related learning threads this week
Note: This Weekly Learning was produced using the Ideas to Life Weekly Learning system. See: Weekly Learning system map