AI Agent Runtime.
Intelligence at the Edge.

Deploy containerized AI agents globally with real-time governance and audit trails. Execute autonomous systems without centralized latency.

Deploy Agent

The AI Agent Challenge

Latency Kills Autonomy

Agents making decisions round-trip to centralized LLM APIs. Real-time autonomy becomes impossible.

No Governance

Deploying autonomous agents at scale without audit trails, policy enforcement, or decision tracking.

Cost & Data Risk

Sending sensitive data to external LLM services for every inference. Compliance exposure. Cost at scale.

No Offline Operation

Internet outages halt autonomous execution. No fallback policy enforcement at the edge.

Model Lock-in

Architecture tied to single vendor APIs. Difficult to switch models or run private LLMs.

Why This Matters

Today's AI applications require autonomous decision-making at the edge. Latency-sensitive fraud detection, real-time customer interactions, and distributed agent networks need inference and execution happening locally, not miles away at data centers. AI Agent Runtime brings containerized LLMs and agent frameworks to the edge.

ConnectEdge AI Agent Runtime

📦

Containerized Agents

Deploy agents in containers. Bring your own models. No vendor lock-in.

Sub-100ms Latency

Inference at the edge. No round-trip to external APIs. Real-time autonomy.

📋

Built-in Governance

Every agent decision logged, audited, and policy-enforced in real-time.

🔒

Private Model Execution

Run proprietary LLMs. Keep training data offline. Zero external exposure.

📡

Offline Resilience

Agents operate independently. Sync when connection resumes. No downtime.

🔄

Model Agnostic

Run any open-source or proprietary LLM. Switch models without re-architecting.

How It Works

[AI Agent Runtime Architecture]
Flow: Agent Container → Policy Engine → Local LLM Inference → Decision Execution → Audit Log → Global Sync

Each edge location runs the full agent stack independently. Policies are enforced locally. Decisions are logged in real-time. No centralized bottleneck.

Use Cases

🚨 Fraud Detection

Challenge: Detect and block fraud in milliseconds. Round-trip to ML API adds too much latency.

Outcome: <50ms fraud decision. Real-time block. Full audit trail.

🤖 Autonomous Customer Service

Challenge: Real-time agent responses. Knowledge base at the edge. No external API dependency.

Outcome: <100ms responses. Governed decisions. Offline operation.

⚙️ Predictive Operations

Challenge: Anomaly detection across distributed infrastructure. Private model execution.

Outcome: Proactive alerts. Zero data exfiltration. Real-time decisions.

Proven Results

300+
Edge Locations
<100ms
Inference Latency
99.99%
Uptime SLA
0
Data Exfiltration
10x
Cost Reduction vs API
24h
Deployment Time

Ready to Deploy AI Agents at the Edge?

Start with a governance assessment. Understand your architecture and compliance requirements.

Deploy Agent

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