Versioned Workflow
Tracking iterations of an automation with release identifiers so you can audit, rollback, or run experiments safely.
A versioned workflow assigns release identifiers to automation logic—prompts, rules, and code—so you can track, audit, and rollback changes safely.
Teams use versioning to run experiments, compare performance, and recover quickly from regressions. It makes changes observable and reversible.
In practice, versions tie to configs, prompts, and code branches, with logs tagged by version. This reduces risk and supports compliant change management.
Frequently Asked Questions
What should be versioned?
Prompts, rules, workflows, and dependencies. Tag releases with IDs and store configs in version control.
How do I roll back a workflow?
Deploy the prior version and monitor metrics. Keep artifacts and configs so rollback is fast and predictable.
Can I A/B test workflow versions?
Yes—route traffic by percentage or cohort, compare metrics, and pick winners. Keep isolation to avoid cross-talk.
How do I audit changes?
Log who changed what and when, with diffs and version IDs. Store prompts/rules with history for compliance.
What metrics should I watch per version?
Success rate, latency, cost, error types, and human escalations. Compare to baseline before full rollout.
How granular should versions be?
Granular enough to isolate changes but not so noisy that tracking is burdensome. Major behavior changes need new versions.
How do I prevent version drift?
Centralize configs, enforce code review, and use automated tests. Retire old versions to reduce surface area.
Does versioning slow teams down?
It adds discipline but speeds recovery and experimentation. Automate tagging and rollout to reduce friction.
Can workflows reference library versions?
Yes—pin dependencies and prompts. Keep dependency versions in lockfiles to make runs reproducible.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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