06 · Conclusion
What seems robust#
Use this model as mechanisms and constraints, not as a forecast.
Across reasonable assumptions, a few claims stay stable:
- The unit of value is the system, not the model. Most operational outcomes depend on tools, data access, evaluation, and governance as much as baseline capability.
- Capability and reliability diverge. Demonstrations and benchmarks may indicate what is possible; they do not, by themselves, determine what is safe or economical to deploy.
- Measurement is the control surface. Without instrumentation and evaluation, iteration becomes drift and failures become anecdotal.
- Scope is an engineering variable. Expanding tool access and task horizon tends to expand both capability and risk; neither scales for free.
Use it for:
- Strategic reasoning: identifying which constraints are likely to bind (measurement, integration, governance, trust) and where to invest.
- System design: structuring workflows so that state, permissions, and evaluation are explicit rather than implicit.
- Risk assessment: enumerating failure modes that emerge from autonomy, tool access, and hidden state.
What is uncertain#
The model is fragile where feedback loops and auditability cannot be established.
Uncertainties include:
- Reliability scaling: whether multi-step reliability improves fast enough to justify broader autonomy in high-cost domains.
- Attribution quality: whether organizations can reliably connect outcomes back to specific system behaviors and then produce durable regression tests.
- Governance scalability: whether permissions, approvals, and incident response can keep pace with compressed iteration cycles.
- Domain dependence: the extent to which any conclusions transfer across environments with different liability profiles, privacy constraints, and data availability.
Don’t use it for:
- Prediction or timelines.
- Ranking individual models absent a specific system context.
- Declaring universal applicability of agentic approaches.