counter_3 Frontiers — Evolve through Controlled Experiments

Your team is generating significant contributions using agents. This engagement helps your organization experiment without losing control.

Product and Architecture teams who want to push boundaries while maintaining the stability and quality their users expect.

Your team is ready to clear their backlog once and for all, and deliver everything their customers have ever asked for.
You have many ideas on what to build next, but to move with structure.

Your org experiments safely. You have the processes and infrastructure to explore new AI capabilities without putting your core business at risk.

We run at few experiments together from hypothesis through decision, adopted or reverted.

Activities

Survey and identify opportunities that align with your product strategy and technical architecture.

Identify and catalog refactoring opportunties, both the low hanging fruits and the heavy lifts
Find components “everyone’s afraid to touch” that could benefit from agent-assisted refactoring
Uncover architecture debates that can become empirical
Size efforts, determine business value and devise high-level roadmap
Setup solution architecture documents and review processes
Extend Community of Practice to all stakeholders

Run controlled experiments. Build the infrastructure and processes that let you try new approaches without risking production systems.

Document hypotheses, success criteria and review timelines
Build and deploy infrastructure to support parallel explorations, including reporting metrics
Learn subagents and agent orchestration
Build minimal versions of competing approaches to compare actual metrics
Track experiments in long-running branches across multiple repositories
Conduct periodic experiment reviews
Ensure effective use of version control to track decisions with observations, assumptions, and arguments

Integrate successful experiments into your standard practices. Update your architecture and workflows to incorporate what you’ve learned.

Make explicit decisions: adopt the new pattern or revert to the old one
Update architecture documents and guidelines based on empirical evidence
Re-evaluate past decisions as new data is collected from experiments
Enable product and architectural data-driven decisions based on production metrics
Establish feedback loops that compound improvements back to the team
Periodically publish successful and aborted experiments

Let's discuss how to experiment with AI capabilities in a controlled way.
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