Harness Engineering for AI Agents
A definition-first playbook for Harness Engineering: wrapping AI agent execution with context boundaries, authority boundaries, evidence capture, plan/confirm/trace records, rollback, remediation, deterministic delivery, and accepted outcome.
Harness Engineering is the discipline of wrapping agent execution with lifecycle boundaries: context boundary, authority boundary, evidence capture, plan/confirm/trace records, rollback, remediation, configuration, and accepted outcome. It is the execution boundary layer behind Deterministic Delivery, not a replacement for prompt quality.
Why ordinary model/tool governance is insufficient
Prompt engineering controls how intent is expressed to the model. Harness Engineering controls the execution boundary around the work. A prompt cannot substitute for context boundaries, authority boundaries, evidence capture, plan/confirm/trace records, rollback paths, remediation closure, configuration state, or accepted outcome state.
White paper source context
This playbook is a practical reading of the GAIC white paper's lifecycle-responsibility argument. For this route, the relevant responsibility objects are Context boundary, Authority boundary, Evidence chain, Decision trace, Accepted outcome, Remediation closure. RCCS-M and ALCS are used as source vocabulary for governance coverage and lifecycle coherence; this page does not add scores or become legal advice, certification, procurement guidance, or a vendor assessment.
Lifecycle governance checklist
- Separate prompt controls from harness controls: the prompt expresses intent, while the harness governs execution boundaries.
- Define the context boundary so active, stale, background, and cross-project context are separated.
- Define the authority boundary before consequential action or tool use.
- Capture evidence that supports review, replay, dispute, and remediation.
- Represent plan, confirmation, and trace as durable lifecycle records.
- Define rollback and remediation behavior before failure occurs.
- Record accepted outcome state and the human role that can accept or reject work.
- Link harness configuration to Deterministic Delivery so lifecycle state can be inspected after execution.
Related Missing Regulatory Objects
RCCS-M / ALCS relevance
RCCS-M is relevant because a harness can make lifecycle responsibility objects explicit. ALCS is relevant because the harness must keep those objects coherent across execution, review, dispute, remediation, and closure.
Protocol path: MPLP as one option
MPLP and Cognitive OS can be understood as possible paths for protocol semantics and runtime state around harnessed agent work. They are not required implementations.
Boundary statement
This playbook is an author-analytical governance guide. It is not legal advice, legal compliance proof, certification, regulator-approved guidance, vendor ranking, or procurement recommendation.