JieGou has evolved.
Since this post was published, JieGou has pivoted from an AI automation platform to an AI-powered operations company delivering managed marketing and operations services. Learn about our managed services →
The Problem Both Solve
Enterprise buyers have a legitimate concern: autonomous AI agents are unpredictable. They can reason, plan, and take actions – but how do you ensure they operate within acceptable boundaries?
Two approaches have emerged:
- Agent Scripts (Salesforce) – a purpose-built scripting language that combines AI creativity with deterministic control
- Governed Autonomy (JieGou) – 10-layer governance infrastructure that sets boundaries, and agents reason freely within them
Same concern. Different philosophies.
Agent Script: The Code-First Approach
Salesforce’s Agent Script, introduced in Agentforce Spring ’26, lets developers write scripts that control agent behavior deterministically. Think of it as a programming language for agent control:
- Developers define agent behavior line-by-line
- Scripts combine AI reasoning with deterministic checkpoints
- The audit trail shows “it did what the script said”
- Changes require script updates (code changes)
This approach appeals to developer-driven organizations where engineering teams own agent behavior. It provides fine-grained, predictable control – at the cost of per-agent scripting effort.
Governed Autonomy: The Infrastructure-First Approach
JieGou’s approach doesn’t script agent behavior. Instead, it creates governance boundaries through 10 infrastructure layers:
- Identity & Authentication – every agent has verified identity
- Encryption – data protected at rest and in transit (AES-256-GCM)
- Data Residency – configurable where data lives
- Environment Management – isolated execution environments
- RBAC – 6 roles, 20 granular permissions
- Escalation Protocols – agents defer to humans when conditions are met
- Tool Approval Gates – per-tool, per-role approvals before execution
- Audit Logging – 30 action types, immutable log
- Compliance Timeline – chronological governance event record
- Evidence Export – 17 TSC controls, auditor-ready packages
- Regulatory Compliance – EU AI Act, NIST, HIPAA, SOX, FedRAMP presets
Agents are autonomous within these boundaries. They can reason, adapt, and respond to novel situations – but they can’t exceed their governance envelope.
Who Controls What
The key difference is who owns agent governance:
| Agent Script | Governed Autonomy | |
|---|---|---|
| Owner | Developers | Compliance teams |
| Control mechanism | Write code | Configure policies |
| Change process | Code review + deployment | Policy update in Operations Hub |
| Scaling model | One script per agent behavior | Boundaries apply to all agents |
In compliance-focused enterprises, the people who understand regulatory requirements are compliance officers – not developers. An infrastructure approach lets compliance teams configure governance directly, without a development bottleneck.
When to Choose Each Approach
Agent Scripts make sense when:
- Your organization is developer-driven
- You need fine-grained, per-behavior control
- You’re fully committed to the Salesforce ecosystem
- Your agents have highly predictable, narrowly scoped behaviors
Governed Autonomy makes sense when:
- Compliance teams need direct governance control
- You need agents that can adapt to novel situations
- You want governance that scales across many agents
- You need regulatory mapping (EU AI Act, NIST, SOX)
- You use agents from multiple vendors
The Regulatory Dimension
Regulators don’t care about scripts. They care about governance boundaries.
The EU AI Act requires risk management systems, record-keeping, and technical documentation. The question is: “Did your AI system operate within its governance boundaries?” – not “Did your AI system follow its script?”
Governed Autonomy maps directly to regulatory requirements because it’s infrastructure, not code. Evidence export generates audit-ready documentation for each compliance framework. Script execution logs prove what the agent did – but governance traces prove the system was governed.
Compare the approaches at Governed Autonomy. Explore the 10-layer stack at Governance Stack.