full-cycle-research

Automate deep research workflows by coordinating 8+ parallel research agents to explore best practices, docs, and security patterns before coding. This structured approach captures decisions in a knowledge graph and delivers a reusable research plan to reduce implementation risk and rework.

Driving architectural decisions for unfamiliar technology stacks requires parallel research agents to evaluate planning, security, and implementation considerations upfront. Using a phase-driven workflow ensures traceable decisions and de-risks high-stakes initiatives before any code is written.

Enforce an end-to-end governance workflow by integrating Agent Harness and Compound Engineering to guide teams through brainstorming, planning, deep research, and peer review. This phase-driven process ensures traceable decisions and captures learnings in a knowledge graph for future features.

You can coordinate multiple agents for parallel deep research and spec flow analysis by using a phase-driven workflow that includes Define, Create Plan, Deep Research, and Execution phases. This ensures comprehensive evaluation of best practices, docs, and security patterns before implementation.

This deep research workflow does not require specific external dependencies or components to run. It operates as a standalone skill that integrates tightly with your existing engineering workflows and knowledge graph capture mechanisms to preserve learnings.

Upfront parallel research prevents misaligned implementations and costly rework by exploring best practices, docs, and real-world examples before coding. It delivers a reusable deep-research plan and captures decisions in a knowledge graph, allowing teams to commit with confidence.