autonomous-chain-controller

You can automate multi-step AI workflows by using an orchestration controller that detects the appropriate skill chain and inserts verification quality gates between every step. This ensures correct output and automatically retries or escalates on failures to maintain reliability.

The best way to coordinate sequential AI skills is using automatic chain detection, which analyzes a vague request like building a feature and selects an ordered skill chain such as brainstorming, planning, TDD, and verification. It enforces quality gates and handles failure recovery automatically.

Yes, automatic chain detection analyzes vague user requests such as building a login page and selects the appropriate ordered skill chain. It determines the necessary sequence like brainstorming to debate to plan-and-execute to TDD to verify, then orchestrates the entire workflow sequentially.

Failure recovery in multi-step task orchestration monitors for premature completion, scope creep, and repeated gate failures. It automatically retries the failed step, invokes a think-twice mechanism, or halts for an autopsy, keeping the sequential workflow reliable without manual intervention.

Multi-step skill orchestration applies specifically to tasks that require three or more sequential skills such as feature development, bug fixing, or content creation. For tasks needing fewer than three skills, this automated chain controller approach is not required.

Multi-step workflows fail with scope creep and repeated gate failures due to unmonitored skill execution. An autonomous controller detects these issues, logs TODOs, checks for regressions, and applies automatic retry logic or escalation to higher-level handlers to resolve the failures.