Enterprise Agentic AI Operating Platform
The production backbone for building, orchestrating and operating enterprise AI agents — not another agent demo.
Role: Lead architect — requirements, architecture, data model, API contracts and full delivery blueprint.
The problem
Teams can prototype an agent in an afternoon, then spend months bolting on the things production actually needs — tool permissions, memory, human approval, reliability, evaluation and an audit trail. This platform is the operating layer that makes agents safe to run at enterprise scale, with a clean separation between UI, API, orchestration, data and infrastructure.
Business Context
Enterprise teams building agents on top of a shared operating layer needed something a security or platform team would actually let into production, not just a working demo. Spire Digi Solution needed the underlying registry, authorization and audit primitives that every agent built on the platform would inherit by default, instead of re-implementing tool permissions and approval flows per agent.
Architecture
Key capabilities
Typed agent & workflow registry with full versioning
Human-in-the-loop approval gates for high-impact actions
Default-deny tool authorization with least-privilege scopes
Real-time run streaming over WebSockets with a complete audit trail
Multi-tenant isolation across data, vectors and tool permissions
CI-gated evaluation that blocks prompt/model regressions before release
Security & Risk
The risk isn't the model — it's what an autonomous agent is allowed to do with production access. This platform closes that gap with default-deny tool authorization, human-approval gates for high-impact actions, and multi-tenant isolation across data, vectors and tool permissions, so a compromised or misbehaving agent has a bounded blast radius by design.
Outcome
A clean operating layer that a security or platform team can actually reason about — every agent action is authorized, approved where it matters, and logged, instead of trusted by default.
Challenges
Balancing autonomy against control was the central tension: agents needed to act — call tools, run workflows — fast enough to be useful, while every high-impact action still had to route through human approval and default-deny authorization scopes without becoming an approval bottleneck. Multi-tenant isolation across data, vectors and tool permissions had to hold everywhere at once, including the registry and evaluation layers, not just storage.
Lessons Learned
An agent's real trust boundary isn't the model, it's the operating layer around it. The registry, the authorization scopes and the approval gates are what actually bound the blast radius of a compromised or misbehaving agent — not anything the model does differently on its own.
Architecture pillars
Designed and reviewed against all ten — see how I evaluate every architecture.
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Technology
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