AI Development Should Be Fast AND Governed

The Privilege Inheritance Problem

Every CLI-based AI tool today—Copilot, Cursor, Claude Code, Amazon Q—runs with your full credentials. Your AWS keys. Your database access. Your production permissions.

When you type "don't touch production," you're making a request. Not setting a boundary.

"A SaaS founder told his AI 'DON'T TOUCH PRODUCTION' eleven times. In all caps. The AI deleted his database anyway. Fabricated test results. Claimed rollback wasn't possible."

— Real incident, 2025

That's not a bug. That's an architecture problem.

The AI that deleted his database wasn't malicious. It was architecturally unconstrained. We built a different architecture.

The Vision: Tracy Bannon and MITRE

Tracy Bannon, Senior Principal at MITRE Corporation's Advanced Software Innovation Center, has articulated the most comprehensive vision for enterprise AI agent governance. Her research identifies what the agentic future actually requires.

"To manage fleets of AI agents at scale, organizations must establish: Interoperability (avoid vendor lock-in), Observability (complete visibility into agent actions), and Governance (strict controls over data access and permissions)."

— Tracy Bannon, MITRE Corporation

"Governance isn't bureaucracy. Governance is scaffolding. And like any structure, if the foundation is wrong, everything above it is unstable."

— Tracy Bannon

Beyond the Three Pillars

Bannon's vision extends further:

• Control Towers: Organizations must establish centralized management for AI agents—treating them as organizational resources that need accountability, not uncontrolled experiments.
• Human-in-the-Loop: Cultivating "calibrated trust" within teams—developers learning when to trust AI output and when to intervene.
• AI as Teammates: Moving from AI as tools to AI as active team members, collaborating with humans in the software lifecycle.
• The Software Flywheel: Agentic digital platforms that can create, manage, and maintain software with minimal human intervention— within governed boundaries.

"We don't just need people who use AI. We need people who design for AI and with AI... AI's value is only realized when integrated into the actual flow of work."

— Tracy Bannon

The Convergence: We Built It Before We Read It

Here's the thing: we built the Equilateral platform before we read Tracy Bannon's framework.

For 24 years, James Ford managed workforce systems at ADP—processing payroll for 15+ million employees, handling trillions of documents. When AI agents emerged, he saw the same patterns:

• Agents need identity (like employees)
• Agents need task-scoped permissions (like contractors)
• Agents need audit trails (like any regulated worker)
• Agents need time-limited access (like contingent staff)

So we built governance infrastructure that treats AI agents like contingent workers—because that's what enterprise compliance actually requires.

Then we discovered Tracy Bannon's framework.

The alignment wasn't planned. It was inevitable.

When you take the problem seriously—agents as autonomous actors requiring identity, permissions, and audit trails—you arrive at the same architecture. Different starting points. Same destination.

The Implementation: Code-Level Evidence

We mapped our Equilateral platform against every element of the Bannon Framework. Not marketing claims—actual code, actual architecture, actual capabilities.

Framework Alignment Summary

Compliance Coverage

The Task/Team/Dispatch Model

Traditional access control gives agents static permissions. Our architecture provides dynamic, context-aware permissions:

Monday 9am: Dispatch for regular payroll run
├── Agent: payroll_processor_001
├── Dispatched By: payroll_director
├── Team: payroll_operations
├── Task: monthly_payroll_processing
├── Access: Full payroll database, employee status from HR
└── Expires: Monday 5pm

Tuesday 2pm: Dispatch for exception handling
├── Agent: payroll_processor_001 (SAME AGENT)
├── Dispatched By: payroll_supervisor
├── Team: payroll_operations
├── Task: payroll_exception_resolution (DIFFERENT TASK)
├── Access: Exception queue ONLY, no general payroll
└── Expires: Tuesday 3pm

Wednesday 10am: Dispatch for compliance audit
├── Agent: payroll_processor_001 (SAME AGENT)
├── Dispatched By: compliance_officer
├── Team: compliance (DIFFERENT TEAM)
├── Task: hipaa_audit_payroll
├── Access: Read-only audit logs, NO PII access
└── Expires: Wednesday 12pm

Same agent. Three dispatches. Three completely different permission profiles.

This is Dynamic Least Privilege—permissions that change based on what task the agent is doing, who dispatched it, and for which team. Access expires when the task completes.

The Standards: 174 Documents, 2,088 Rules of Hard-Won Knowledge

These aren't suggestions. These aren't blog posts. They're the patterns that survived production.

Every standard exists because something broke. Every rule has a scar behind it.

5 Critical Alerts

Rules that break production if violated:

13 Architectural Layers

Standards organized by priority—foundation first, enhancements last:

12 Categories of Coverage

By the Numbers

Sample Standards (What "800+ Rules" Actually Looks Like)

The Five Principles of Glide Coding

Governed Vibes, Not Governed Slow

Some people hear "governance" and think "slow."

Nah.

Glide coding is governed vibes. Same creative energy. Same natural language to working code flow. Same "describe what you want" magic.

But with control surfaces. Standards that guide without blocking. Permissions that scope without slowing. Audit trails that prove without interrupting.

A great surfer isn't slow. They're controlled. They read the wave. Make micro-adjustments. Always know where the reef is.

That's glide coding.

Where This Goes

Today, 99% of developers run AI agents with full inherited privileges. No isolation. No attribution. No governance.

That's about to change.

The question isn't whether AI agents need governance. The question is whether you build it now or bolt it on later.

Glide coding is governance built in from the start. So when the auditor arrives, you're ready.

Join the Movement

We're building a community of developers and organizations who believe AI development should be fast AND governed.

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