When to Choose Each Option
Clear guidance based on your specific situation and needs.
Our Recommendation
These are not competitors — they are two different layers of the same release, and the honest answer is 'it depends on what you own.' Reach for WorkflowAgent when you are building the agent loop yourself: you call your own model and tools, the run is long enough to outlive a serverless timeout or a deploy, and you need automatic retries, resume-from-checkpoint and approvals that survive suspension without hand-rolling a state machine. Reach for HarnessAgent when you do not want to build the loop at all: you want to embed a proven coding agent like Claude Code or Codex behind one SDK surface, get its workspace tools, compaction and permission flows for free, and run it sandboxed so the host stays safe — accepting that the harness packages are still marked experimental at launch. The two even compose: a HarnessAgent can be driven inside durable workflow infrastructure when an off-the-shelf agent's runs must also survive restarts. The framing Context Studios uses with clients is governance-first — sandbox and approve any third-party runtime (HarnessAgent's default), and make any long-running, business-critical loop you own durable and observable (WorkflowAgent). Pick by ownership and risk, not by which name appeared first in the changelog.
- Choose WorkflowAgent when...
- You own the agent loop and call your own model and tools, and the run must survive serverless timeouts, restarts and deploys
- You need automatic retries and resume-from-checkpoint without hand-rolling your own state machine
- You want every tool call visible as a discrete, observable workflow step in your dashboards
- You need human approvals that pause the agent and can resume hours later, surviving suspension
- Choose HarnessAgent when...
- You want to embed a full off-the-shelf coding agent — Claude Code, Codex, Deep Agents, OpenCode or Pi — behind one AI SDK surface
- You need built-in workspace access, coding tools, compaction and permission flows without building them yourself
- Sandboxed isolation of the agent runtime is a hard, non-negotiable requirement
- You want both generate() and stream() and a clean drop-in to useChat and existing AI SDK UI surfaces