Context boundaries
Agents operate within scoped tools, repositories, and data sources configured per role. No unbounded access.
Governance
Governed AI you can defend to audit and procurement
Enterprise agentic AI needs more than a policy appendix. OrqForge shows what context was used, what was recommended, who reviewed it, and why a decision was approved, with retained evidence on every material run.
Enterprise trust
AI you can defend to audit and procurement
Governed learning. Nothing silently changes agent behaviour without explicit approval.
Assist, draft, and auto execution modes per workflow type. Review gates map to stream owners. Stage history, delegation lineage, and input snapshots export for assurance.
Governance details →OrqForge is built for organisations where AI work cannot sit outside the operating model. It is not unmanaged autonomy. It is governed AI that improves operational throughput while keeping humans accountable for material outcomes.
Execution modes
Each workflow type declares how much autonomy agents have:
| Mode | Agent behaviour | Human role |
|---|---|---|
| Assist | Provides analysis and recommendations only | Human takes all actions based on agent output |
| Draft | Produces deliverables and waits for explicit approval | Reviewer must approve before anything is published |
| Auto | Executes well-understood repeatable work | Reviewer approves or rejects; publish proceeds on approval |
Review gates
Every workflow type specifies who reviews output, by role label, not user ID. Review can be skipped for informational work, handled by an independent AI reviewer for non-destructive workflows, or routed to a human for code changes, customer-facing output, or sensitive decisions.
Agents can self-review, but human review is always available and configurable. An orchestrator can produce a report and route it to a human for final approval, with the review path adjusted per workflow type.
Evidence and audit trail
- Stage history: every step from ingest through complete is recorded and inspectable
- Input and output snapshots: what went in and what came out at each stage
- Review decisions: who approved, who requested changes, and why
- Delegation lineage: which agent delegated to which specialist
- Resume lineage: when runs are resumed, the full chain is preserved
Governed learning
Learning does not happen silently. Corrections from reviewer-approved versions become knowledge entries, pending admin review before agents can use them. Humans can teach the system directly via the platform or Teams. Evolution proposals for prompt or rule changes require explicit approval.
This is how OrqForge improves over time without drifting away from your operating model. Accuracy compounds, but only through governed, inspectable change.
Why this matters for enterprise
Procurement, risk, and operations leaders need clarity on three points: what context agents can use, where deterministic controls apply, and who approves material outcomes. OrqForge is designed to answer all three, on every run, after the event.
Quality gates
Validation before publish. Review rounds with feedback loops. Escalation when agents cannot proceed.
Named accountability
Review gates map to role labels. Stream owners assign humans. Decisions are attributed, not anonymous.
Governance questions
What execution modes are available?
Assist: agents provide analysis and recommendations; humans take all actions. Draft: agents produce deliverables and wait for explicit approval before publish. Auto: agents execute repeatable work and proceed once the reviewer approves.
Can agents review their own work?
Yes, optionally. OrqForge supports independent AI review for non-destructive workflows, human review for sensitive output, or no review for informational work. The mode is configured per workflow type.
What evidence is retained?
Every run keeps stage history, input and output snapshots, review decisions, and delegation lineage. Teams and auditors can inspect what happened, who approved it, and on what basis.
How does the learning loop stay governed?
Lessons extracted from completed runs start as pending review. Human teaching submissions require admin approval. Evolution proposals for prompt or rule changes must be explicitly approved before they influence agent behaviour.
Discuss your governance requirements
Tell us about your review paths, approval workflows, and assurance needs. We'll show you how OrqForge fits.