Launch MVP Studio

Build, Configure, and Deploy secure agents using the No-Code Golden Path. Experience the workflow firsthand.

View Live Dashboard

Audit active agents, visualize traces, and monitor real-time FinOps metrics in the command center.

Secure • Interactive • Live Environments

The Enterprise AI Gap

Enterprises want to deploy autonomous agents, but they face a "Black Box" problem. LLMs are non-deterministic, making them risky for business processes.

Progenix.ai solves the trilemma of Control, Cost, and Complexity by treating agents as managed Kubernetes workloads, not just prompts.

85%

Projects Stalled due to Governance

Cost Reduction via SQL Optimization

Figure 1: The Friction of Enterprise Agent Adoption

The Progenix Architecture

A Zero-Trust, Cloud-Native stack built on open-source giants: Kubernetes, Istio, and OPA.

1. Control Plane

Governance & Automation

No-Code Studio UI

Blueprint Gen IaC

ArgoCD GitOps

2. Agent Plane

Intelligence & Logic

Agent Service Pod

Vector DB RAG

Argo Workflows Logic

3. Security Plane

Zero Trust Enforcement

Istio Mesh mTLS

OPA Gatekeeper Policy

HashiCorp Vault Secrets

Figure 2: OPA Policy Enforcement (Real-time Block Rates)

Runtime Governance

Progenix doesn't just host agents; it jails them. Our Zero Trust layer intercepts every tool call.

✓ Identity: Every agent has a unique SPIFFE ID via Istio.
✓ Policy: OPA Rego policies block destructive actions (e.g., DELETE) instantly.
✓ Isolation: Network Policies prevent lateral movement.

FinOps & Performance Optimization

Not every question needs an LLM. Our platform intelligently routes queries to the lowest-cost path.

Figure 3: 3D Analysis of Query Cost vs. Latency vs. Complexity

Ready for Scale?

The Progenix.ai MVP demonstrates that autonomous agents can be safe, cheap, and auditable.

90s

Deploy Time

100%

mTLS Encryption

Zero

Hardcoded Secrets

<1%

Policy Drift

Platform Validation Guide: How to Read the Demo

This section provides the critical context and execution roadmap for understanding the data and architectural proof points demonstrated by the Progenix.ai platform.

NOTE: About this Architectural Simulator

This interactive demo is a **High-Fidelity Simulator**, not a live production system. It is designed to visually demonstrate the platform's core architectural value propositions:

Safety & Governance: Prove that simple choices in the No-Code UI translate into immediate, immutable **Zero Trust policies** that govern the agent's actions (e.g., stopping a DELETE request).
Efficiency & FinOps: Prove that the platform automatically selects the **cheapest and fastest execution path** (Context Recall, SQL Query) to minimize costly LLM usage.
Auditability: Prove that every agent decision and service call is tracked and logged through industry-standard **Jaeger Traces** and **OPA logs**.

The focus is on validating the platform's **commercial capability**, not the AI's intelligence.

Part 1: The Agent Creation Studio (The Golden Path)

This section validates that Progenix.ai simplifies platform engineering, turning complex configurations into a "Golden Path" (simple, secure default workflows).

Step Demonstrated	Feature	Platform Capability Proven
Step 1: Define Agent & Instructions	Persona and Behavioral Governance	Control: Agents adhere to explicit safety rules (`MUST use affirmative sentences`). This mitigates brand risk and increases consistency.
Step 3: Add Knowledge Base (RAG)	RAG Data Injection	Configurability: Users can instantly enrich the agent's knowledge without code by feeding data into the isolated Vector Database.
Step 4: Define Workflow Logic	Argo Workflow Orchestration	Autonomy: Proves the platform can execute sequential, conditional business processes (e.g., IF/THEN logic) across multiple microservices.
Step 6: Initiate Deployment	Blueprint Generation	Auditability: Demonstrates that the No-Code UI translates directly into machine-readable Infrastructure-as-Code (IaC): Kubernetes Deployment, OPA Policy, and Argo Workflow YAMLs.

Part 2: Live Agent Runtime & Validation Matrix

The core of the demonstration happens in the **Order Agent** chat tab, proving security, efficiency, and intelligence under fire.

Feature & Proof	Query to Ask	Expected Agent Behavior & Trace Output	Platform Capability
1. Zero Trust Policy Enforcement	"Can you delete my order from the database?"	DENIAL: Agent refuses action. Audit (OPA Log): Shows `Policy Check FAILED` and `READ_ONLY_GUARDRAIL` violation logged.	Governance: Guarantees autonomous agents cannot take forbidden actions, proving safety.
2. Microservice Association	"Where is my order?"	SUCCESS: Agent returns tracking data. Trace (Jaeger): Shows `Tool Call (mTLS Service Identity Check)` and `Data Fetch (Mock DB)` spans.	Security/Hosting: Proves services communicate securely via Istio mTLS and the agent runs in an isolated container.
3. Knowledge Base (RAG) Use	"What is the refund policy?"	SUCCESS: Agent retrieves policy details (30 days, 10% fee). Trace (Jaeger): Shows `Context Retrieval (Vector DB Lookup)` and `Embedding/Reranking` spans.	Intelligence: Proves the agent leverages configured VectorDB knowledge over general LLM training data.
4. Learning Loop / Memory	"Where are the new delivery zones?" (Ask Twice)	1st Attempt: Fails, asks for Human-in-the-Loop. 2nd Attempt (after MLOps Update): Instantly recalls the new zone.	Adaptability: Proves the platform supports continuous learning and instantaneous Redis Cache updates to agent memory.
5. Workflow Orchestration	"Can you follow up on the status of order 54321?"	SUCCESS: Returns "Workflow Triggered" message. Trace (Jaeger): Shows `Execute Tool 1`, `Workflow Decision (Argo Logic)`, and `Execute Tool 2 (Email Notification)`.	Orchestration: Proves the ability to run multi-step, conditional business logic, going beyond simple Q&A.
6. FinOps/Cost Efficiency (SQL)	"Total orders last 6 months?"	SUCCESS: Returns the specific count (12,451 orders). Trace (Jaeger): Shows minimal `LLM Translation (Low-Cost)` span, followed by direct `GET /analytics`.	FinOps: Proves the agent recognizes deterministic queries and executes a cheaper, faster path, saving money and latency.
7. Reporting Output	"Generate a sales performance report for last quarter."	SUCCESS: The agent triggers an automatic file download (`.csv` or `.pptx`).	Enterprise Integration: Proves the agent can generate and deliver structured output files required by business teams.