RAVP: Regulated Agent Vending Platform

RAVP UI

Inspiration

Enterprise AI adoption faces a critical challenge: how do you deploy AI agents at scale while maintaining governance, security, and compliance?

As organizations rush to integrate LLMs like Gemini into production systems, they encounter real-world barriers:

No centralized control over which agents can access what tools
Missing audit trails for AI decisions in regulated industries (finance, healthcare, government)
No emergency kill-switch when agents malfunction or go rogue
Difficulty managing agent-to-agent collaboration at scale
Complex multi-cloud deployment requirements across GKE, AKS, and EKS
API keys and credentials baked into code instead of runtime configuration

We built RAVP (Regulated Agent Vending Platform) to solve this: a production-grade platform where you can create, govern, and deploy Gemini-powered AI agents with enterprise controls baked in from day one. Think of it as "Kubernetes for AI Agents" - a control plane that manages the full lifecycle of governed, auditable, killable agents.

What it does

RAVP is a governed agent orchestration platform powered by Google Gemini that enables enterprises to:

Core Capabilities

Create AI Agents - Define agents via intuitive Streamlit UI or API with purpose, allowed tools, risk tier, and policies
Enforce Governance - Built-in RBAC, Rego policy evaluation, comprehensive audit logging, and emergency kill-switch
Deploy Anywhere - One-click deployment to GKE, AKS, EKS, or local Docker with auto-generated Kubernetes manifests
Agent Mesh - Agents discover and invoke each other through capability-based routing (A2A protocol)
Runtime LLM Config - Switch between Google AI Studio, Vertex AI, OpenAI, or Anthropic per environment without rebuilding images
Skills Framework - Structured capability system for agent discovery and routing

Real-World Example

Cloud Reliability Agent powered by Gemini:

Monitors GCP incidents via Cloud Monitoring API
Uses Gemini to analyze logs and metrics for root cause diagnosis
Evaluates Rego policies: "Can we auto-remediate this issue?"
Invokes Cloud Healing Agent to execute fixes (with human-in-the-loop approval)
Every action is audited; emergency kill-switch available if needed

Key Features

Agent Registry: Versioned agent definitions with semantic versioning
Tool Gateway: Enforces that agents can only call allowed tools
Policy Engine: Rego-based decision evaluation before critical actions
Audit Store: Immutable log of all agent actions for compliance
Kill-Switch: Circuit breaker to disable agents or models instantly
Multi-Provider LLM: Unified API for Gemini (AI Studio + Vertex AI), OpenAI, Anthropic
Conversation History: Full context retention for interactive agents
Auto-Codegen: Generate agent implementation from YAML definition
MCP Support: Model Context Protocol server for external tool consumers

How we built it

Architecture

1. Control Plane (FastAPI) The central nervous system managing all platform services:

Agent Registry: Stores versioned agent definitions with RBAC (creator, visibility, domains)
Tool Registry: Managed catalog of allowed tools per agent with versioning
Policy Registry: Rego policy evaluation engine
Audit Store: Immutable log of all agent decisions and tool calls
Kill-Switch Service: Emergency disable for agents or models
Deployment Manager: Records and tracks multi-cloud deployments
Mesh Discovery: Agent-to-agent capability routing and invocation
Docker Build Service: Auto-build and push agent images to registries

2. Agent SDK (Python) The enforcement layer that every agent uses:

from org_agent_sdk import RegulatedAgent

agent = RegulatedAgent(
    agent_id="cloud_reliability",
    control_plane_url="http://localhost:8010"
)

# SDK enforces:
# - Kill-switch check before every run
# - Tool calls only from allowed_tools list
# - Policy evaluation before critical decisions
# - Audit logging of all actions

Core components:

RegulatedAgent: Base class enforcing governance
ToolGateway: Restricts tools to allowed list
PolicyClient: Evaluates Rego policies via control plane
AuditClient: Logs all tool calls asynchronously
LLMClient: Multi-provider abstraction
AgentClient: Mesh discovery and A2A invocation

3. Gemini Integration Gemini is the reasoning engine powering agent intelligence:

# Flexible Gemini configuration
from org_agent_sdk.llm_client import LLMClient

llm = LLMClient(
    model="gemini-2.0-flash-exp",
    api_key=os.getenv("GOOGLE_API_KEY"),
    provider=os.getenv("LLM_PROVIDER", "google"),  # or vertex_ai
    endpoint=os.getenv("GOOGLE_API_ENDPOINT"),  # optional custom endpoint
    project=os.getenv("GOOGLE_CLOUD_PROJECT")  # for Vertex AI
)

# Gemini function calling for tool invocation
tools_schema = gateway.get_tools_schema()  # Convert to Gemini format
response = llm.generate(
    prompt=user_query,
    tools=tools_schema,
    system_instruction=agent.purpose
)

Gemini Features We Leverage:

Function Calling: Native tool invocation with structured schemas
Streaming: Real-time response streaming for interactive agents
System Instructions: Inject agent purpose, skills, and constraints
Long Context: Analyze full incident logs and stack traces
Multi-turn: Maintain conversation history for iterative problem-solving
Vertex AI: GCP-native deployment for enterprise customers

4. Platform UI (Streamlit) No-code interface for agent lifecycle:

Create Agent: Form-based agent definition with model dropdown (including Gemini models)
My Agents: Personal agent list with edit/deploy actions
Browse Agents: Agents grouped by domain (Payments, Cloud, Fraud, etc.)
Deploy Agent: Multi-cloud deployment wizard with LLM runtime config
Manage Tools/Policies: Admin interface for registry management
Version History: Semantic versioning with changelog per agent
Interactive Chat: Test agents with conversation history

5. Multi-Cloud Deployment

Auto-generated Dockerfiles: Per-agent containerization
Kubernetes Manifests: ConfigMaps, Secrets, Deployments, Services
Cloud Support: GKE (Google), AKS (Azure), EKS (AWS)
Runtime Config Injection: LLM credentials passed as env vars, not baked into images
Helm Charts: For complex multi-agent deployments

6. Tools Layer Domain-specific tools that agents invoke:

mcp_gcp_tools: GCP incident management, log analysis, metrics
mcp_payment_tools: Payment exceptions, retry logic
mcp_customer_tools: Customer profiles, payment history
mcp_fraud_tools: Fraud scoring, risk assessment
mcp_healing_tools: Auto-remediation actions (restart VMs, scale resources)

Tools can call existing APIs/Apigee proxies with proper auth.

Tech Stack

LLM: Google Gemini (AI Studio + Vertex AI), OpenAI, Anthropic
Backend: Python 3.9+, FastAPI, Pydantic
Frontend: Streamlit
Policy Engine: Open Policy Agent (Rego)
Orchestration: Kubernetes, Docker
Storage: File-based registry (extensible to Cloud Storage, PostgreSQL)
Protocols: REST, Agent-to-Agent (A2A), Model Context Protocol (MCP)
CI/CD: GitHub Actions, Cloud Build

Development Process

Built control plane with FastAPI route modules
Created Agent SDK with governance primitives
Integrated Gemini with function calling and streaming
Developed Streamlit UI with agent creation wizard
Implemented auto-codegen for agent boilerplate
Added multi-cloud deployment with Kubernetes manifest generation
Built agent mesh discovery and A2A invocation
Integrated runtime LLM configuration for multi-tenancy

Challenges we ran into

Challenge 1: Unified Multi-Provider LLM Client

Problem: Support Google AI Studio, Vertex AI, OpenAI, and Anthropic with one interface while respecting their different authentication, function calling formats, and error handling.

Solution:

Built LLMClient abstraction layer normalizing:
- Authentication (API keys vs GCP ADC vs bearer tokens)
- Function calling schemas (Gemini format vs OpenAI format)
- Streaming responses with different chunking strategies
- Provider-specific error codes and retries
Result: Same agent code works with any provider; switch via env vars

Challenge 2: Runtime LLM Configuration Without Rebuilding

Problem: Hard-coding API keys in agent definitions is insecure; baking them into Docker images breaks multi-tenancy (same agent, different customers).

Solution:

Agent definitions specify model as "auto" or model name (no credentials)
LLM config injected as Kubernetes env vars at deployment time: yaml env: - name: GOOGLE_API_KEY valueFrom: secretKeyRef: name: gemini-api-key key: api-key - name: LLM_PROVIDER value: "vertex_ai"
Same Docker image can use AI Studio in dev, Vertex AI in prod, or OpenAI in testing

Challenge 3: Agent Code Generation from YAML

Problem: Manually writing 80+ lines of SDK boilerplate for each agent (load definition, init tools, wire LLM, handle conversation) is tedious and error-prone.

Solution:

Template-based code generation using agents/template/ as base
Jinja2 templating with placeholder replacement:
- {{agent_id}} → actual agent ID
- {{allowed_tools}} → tool import statements
- {{purpose}} → agent purpose string
Auto-generates: agent.py, interactive.py, __init__.py, README.md
Triggered on agent creation or via POST /api/v2/code-gen/generate
Developers can still customize generated code

Challenge 4: Policy Evaluation Latency

Problem: Calling control plane for Rego policy evaluation on every decision adds 50-100ms latency; unacceptable for high-frequency agents.

Solution:

Async audit logging (don't block on writes)
Policy result caching for idempotent decisions
Future: OPA sidecar with policy bundles (pull vs push)
Critical policies still evaluated in real-time; non-critical cached

Challenge 5: Agent-to-Agent Circular Dependencies

Problem: Agent A invokes Agent B, which invokes Agent C, which invokes Agent A → infinite loop.

Solution:

Invocation policy in config/agent_invocation.yaml: yaml cloud_reliability: can_invoke: - cloud_healing cloud_healing: can_invoke: [] # leaf agent, no further invocation
SDK enforces allowlist; blocks unauthorized A2A calls
Max invocation depth limit (default: 5)

Challenge 6: Kubernetes Secret Management Across Clouds

Problem: GCP uses Secret Manager, Azure uses Key Vault, AWS uses Secrets Manager - different APIs, different auth.

Solution:

Generate standard Kubernetes Secrets in manifests (lowest common denominator)
Document external secret operator integrations per cloud:
- GCP: External Secrets Operator + Secret Manager
- Azure: CSI driver + Key Vault
- AWS: External Secrets Operator + Secrets Manager
Deployment wizard injects secrets as env vars from Kubernetes Secrets

Challenge 7: Gemini Function Calling Schema Conversion

Problem: Our tool definitions use Python type hints; Gemini expects JSON Schema format with specific structure.

Solution:

Built converter in ToolGateway.get_tools_schema(): python def to_gemini_schema(tool_func): return { "name": tool_func.__name__, "description": tool_func.__doc__, "parameters": { "type": "object", "properties": extract_params(tool_func), "required": get_required_params(tool_func) } }
Automatically converts all allowed tools to Gemini-compatible format
Handles nested objects, arrays, enums

Accomplishments that we're proud of

🏆 Complete Governed Agent Platform: We built a production-ready system that doesn't compromise on governance, security, or developer experience. This isn't a demo - it's a real platform.

🎯 Gemini Integration Excellence: Seamless support for both Google AI Studio (dev/testing) and Vertex AI (production) with function calling, streaming, and conversation history.

🔒 Security-First Architecture: RBAC, policy enforcement, audit logging, and kill-switch aren't afterthoughts - they're built into the SDK that every agent must use.

⚡ Auto-Codegen Magic: Create an agent in the UI, get 300+ lines of production-ready Python code auto-generated with proper SDK integration, tool wiring, and interactive REPL.

☁️ True Multi-Cloud: Same agent code and Docker image runs on GKE, AKS, EKS, or local Docker with environment-specific LLM configuration.

🤝 Agent Mesh: Agents discover and invoke each other based on skills/capabilities, with invocation policies preventing chaos.

🎨 Developer Experience: Both UI for non-technical users AND SDK for developers; comprehensive docs; sensible defaults; minimal boilerplate.

📊 Real-World Agents: Not just toy examples - we have production-ready agents for:

Cloud reliability and incident response
Payment failure investigation
Fraud detection
Customer support
Multi-cloud healing

🔧 Extensibility: Plugin architecture for tools, policies, and LLM providers; easy to add custom domains and capabilities.

What we learned

1. Gemini API Deep Dive

Function calling is production-ready: Reliable, handles complex schemas, proper error handling
Streaming matters: Real-time responses drastically improve UX for long-running analyses
System instructions are powerful: Injecting agent purpose and skills creates strong persona
Context window is generous: Can analyze full incident logs (10K+ tokens) in one shot
Vertex AI vs AI Studio: Studio is perfect for dev; Vertex AI for prod with better SLA and quotas
Model selection: gemini-2.0-flash-exp provides best balance of speed/quality for agent tasks

2. Governance Cannot Be Optional

You can't trust agents to "do the right thing" - governance must be SDK-level, not agent-level
Kill-switch must be checked before every run, not just at startup
Audit logging must be async; blocking on writes kills performance
RBAC needs to be fine-grained: who can view vs use vs edit vs deploy
Policy-as-code (Rego) is powerful but requires clear input/output contracts

3. Agent Orchestration at Scale

Skills vs Tools separation is critical:
- Skills = what agent can do (incident_investigation)
- Tools = how it does it (get_incident, analyze_logs)
- Purpose = why it exists (24/7 reliability)
Capability-based routing beats hard-coding: Instead of "invoke agent_123", do "find agent with skill=root_cause_analysis"
Invocation policies prevent chaos: Allowlist who can invoke whom; max depth limits
Mesh discovery needs structure: Filtering by domain, persona, skill, risk_tier

4. Multi-Cloud Deployment Realities

Kubernetes is the universal abstraction; cloud-specific features are nice-to-have
Runtime configuration injection is non-negotiable for multi-tenancy
Same image, different env vars = different LLM backends, different customers
Secrets management is still painful; External Secrets Operator helps
Helm adds complexity; start with raw manifests, graduate to Helm

5. Developer Experience Multiplier

Auto-codegen saves hours per agent; developers can still customize
UI + SDK dual interface reaches more users
Conversation history is mandatory for debugging agent behavior
Good defaults matter: model="auto", sensible tool timeouts, built-in retries
Documentation and examples are as important as the code

6. LLM Provider Abstraction Challenges

Every provider has quirks: auth, function calling format, error codes, rate limits
Gemini's native function calling is more reliable than OpenAI's (fewer formatting errors)
Streaming implementations vary wildly; need robust chunk handling
Provider-agnostic code is possible but requires careful abstraction design

What's next for RAVP: Regulated Agent Vending Platform

Near-Term (Next 3 Months)

1. Enhanced Skills Routing with Gemini

Use Gemini to analyze user query and automatically select best agent based on skills
Example: "My payment failed" → Gemini routes to payment_failed agent
Natural language agent discovery instead of manual selection

2. Distributed Tracing

OpenTelemetry integration for cross-agent request tracking
Trace an incident from detection → diagnosis → healing → resolution
Visualize agent-to-agent call graph with latency metrics

3. Cost and Performance Observability

Dashboard showing per-agent Gemini API costs
Token usage tracking and optimization recommendations
Latency p50/p95/p99 for agent responses
Cache hit rates for policy evaluation

4. Policy Marketplace

Shareable Rego policies for common scenarios:
- PCI-DSS compliance for payment agents
- HIPAA compliance for healthcare agents
- SOC 2 audit trail requirements
Community-contributed policies with ratings/reviews

Mid-Term (Next 6 Months)

5. Multi-LLM Orchestration

Single agent dynamically chooses LLM per task:
- Gemini for fast incident triage
- GPT-4 for complex root cause analysis
- Claude for long-document summarization
Cost-aware routing (use cheaper model when possible)

6. Gemini Multimodal Support

Agents process images: architecture diagrams, error screenshots, dashboards
Video analysis: screen recordings of incidents
Structured data: CSV logs, JSON configs

7. Agentic Workflows

Visual workflow builder (drag-drop agent connections)
Conditional routing based on policy evaluation
Parallel agent execution with result aggregation
Human-in-the-loop approval gates

8. GitOps Integration

Agent definitions stored in Git repos
PR-based agent updates with approvals
CI/CD pipeline for agent testing and deployment
Rollback to previous agent versions

Long-Term (Next Year)

9. Federated Agent Mesh

Cross-organization agent discovery (with permissions)
Marketplace for agent templates
Shared tool registry across companies
Industry-specific agent packs (fintech, healthcare, retail)

10. Advanced Governance

Real-time agent behavior monitoring with anomaly detection
Automatic kill-switch triggers based on policy violations
Compliance report generation (GDPR, SOC 2, ISO 27001)
Fine-grained PII handling with data masking

11. Edge Agent Deployment

Lightweight agents running on edge devices
Local Gemini models for low-latency scenarios
Sync with central control plane for updates
On-device policy evaluation

12. Agent Learning and Improvement

Track agent success/failure rates
Use Gemini to analyze failed incidents and suggest tool improvements
A/B testing for agent prompt variations
Automatic retraining based on user feedback

Research Areas

13. Autonomous Agent Swarms

Multiple agents collaborate on complex problems
Self-organizing based on skills and workload
Consensus mechanisms for conflicting recommendations

14. Explainable AI for Agents

Gemini generates human-readable explanations for decisions
Trace decision back to specific policy rules or data
"Why did you choose this remediation?"

15. Agent Security Hardening

Adversarial testing: red team agents trying to break policies
Prompt injection detection and mitigation
Sandboxing for untrusted tool execution

Conclusion

We believe RAVP represents the future of enterprise AI: governed, auditable, multi-cloud, and production-ready. With Gemini as the reasoning engine and comprehensive controls as the foundation, organizations can finally deploy AI agents at scale without sacrificing security or compliance.

Why Gemini?

We chose Gemini as our primary LLM because:

Function Calling: Native, reliable, and well-documented
Context Window: Long context for analyzing full incident logs
Vertex AI Integration: Seamless GCP deployment for enterprises
Multimodal Ready: Prepared for future image/video tool inputs
Cost-Effective: Competitive pricing for production workloads
Google Cloud Synergy: Natural fit for GCP-heavy organizations

Gemini isn't just an LLM provider for us - it's the reasoning engine that makes governed, production-grade AI agents possible.

Contact:

Team: [RAVP team]
Email: [visanthoxd@gmail.com]

Built With

3.11
agent)
api
bash/shell
docker
fastapi
gcr)
gemini
gke)
google-genai)
kaniko
kubernetes
logging
monitoring
open
policy
pydantic
python
rego
streamlit
uvicorn
vertex
yaml

Inspiration

What it does

Core Capabilities

Real-World Example

Key Features

How we built it

Architecture

Tech Stack

Development Process

Challenges we ran into

Challenge 1: Unified Multi-Provider LLM Client

Challenge 2: Runtime LLM Configuration Without Rebuilding

Challenge 3: Agent Code Generation from YAML

Challenge 4: Policy Evaluation Latency

Challenge 5: Agent-to-Agent Circular Dependencies

Challenge 6: Kubernetes Secret Management Across Clouds

Challenge 7: Gemini Function Calling Schema Conversion

Accomplishments that we're proud of

What we learned

1. Gemini API Deep Dive

2. Governance Cannot Be Optional

3. Agent Orchestration at Scale

4. Multi-Cloud Deployment Realities

5. Developer Experience Multiplier

6. LLM Provider Abstraction Challenges

What's next for RAVP: Regulated Agent Vending Platform

Near-Term (Next 3 Months)

Mid-Term (Next 6 Months)

Long-Term (Next Year)

Research Areas

Conclusion

Why Gemini?

Built With

Updates