Agentic AI & Security-First Architecture
A three-layer architecture from signal capture, knowledge graph inference, to autonomous agent execution. Designed for on-premises and air-gapped environments — proven in government, legal, and enterprise mission-critical deployments.
Not Just Answering — Autonomously Completing Tasks
Agentic AI autonomously reasons, plans, and executes complex multi-step tasks. The following four technical pillars — execution loop, multi-layer DLP, agent threat defense, and knowledge graphs — form the foundation of our secure deployments across government, legal, and enterprise environments.
Reasoning, Planning & Execution Loop
The fundamental difference between agentic AI and traditional Q&A AI is the ability to autonomously execute multi-step tasks. Upon receiving a goal, an agent first reasons (analyzing current state and constraints), then generates an execution plan (decomposing the task into sub-steps), and finally invokes tools or other agents to complete each step — dynamically adjusting the plan based on feedback.
- Goal-driven: the agent autonomously decides *how* to reach the goal, not just follow step-by-step instructions
- Multi-agent collaboration: planner, executor, and validator agents each handle distinct roles
- Tool invocation: calling external APIs, databases, search engines, and document generation tools
- Feedback loop: continuously refining reasoning and action strategies based on execution results
Multi-layer DLP Implementation
Data Loss Prevention (DLP) is especially critical in agentic AI environments — an agent's tool-calling and autonomous output capabilities create far greater data exfiltration risk than traditional systems. We implement five-tier progressive protection, achieving high-precision sensitive data detection with minimal latency impact.
- Tier 0–1: Invariant rule enforcement & blacklist lookup (< 1ms)
- Tier 2: Regex pattern matching — Taiwan ID numbers, phone, account patterns, prompt injection signatures (< 5ms)
- Tier 2.5: Embedding similarity matching to detect paraphrased or obfuscated attacks
- Tier 3–4: Behavioral anomaly detection + LLM-as-Judge semantic analysis (for high-risk requests)
Full technical architecture, rule categories, compliance mapping & deployment guide
Read DLP White PaperAI Agent Threat Defense
AI agents face far more complex threats than traditional applications. Referencing the OWASP Agentic AI Top 10 and Agent Threat Rules framework, we identify and defend against ten primary attack vectors — from input-side prompt injection, to tool poisoning and privilege escalation during agent execution, through to output-side context exfiltration.
- Prompt Injection: encoded attacks (base-N, Unicode obfuscation), instruction override detection
- Context Exfiltration: API key leakage, system prompt theft, credential harvesting prevention
- Tool Poisoning & Excessive Autonomy: malicious tool response detection, agent runaway loop circuit-breaking
- RAG Data Poisoning: knowledge base write validation and semantic consistency checks
Full 10-category threat taxonomy, five-tier detection rules, CVE mapping & compliance framework
Read Agent Threat White PaperKnowledge Graphs & LLM Hallucination Reduction
LLM hallucinations fundamentally arise when the model generates without factual anchors. Knowledge graphs provide a structured entity-relationship network as the factual foundation for RAG — so every LLM statement maps to a verifiable graph node, rather than relying on statistical patterns from training data.
- Graph nodes serve as factual anchors, preventing unconstrained LLM generation
- pgvector semantic search retrieves precise relevant subgraphs from the knowledge base
- Cross-system entity resolution ensures the same entity remains consistent across data sources
- Graph updates trigger RAG cache invalidation, ensuring knowledge remains current
On-Premises & Air-Gapped Deployment
Fully On-Premises
All components — application servers, databases, AI inference engines, knowledge graphs, and monitoring tools — deployed entirely within the client's physical environment. No external network connection required.
Air-Gapped Support
Designed for fully air-gapped environments. AI models run local inference; system updates delivered via secure offline media with zero cloud dependencies. Validated in government and high-sensitivity legal environments.
Hybrid Options
Some clients require hybrid flexibility — keeping core data and AI inference on-premises while connecting specific management or collaboration functions to secure cloud services. An LLM Router provides unified routing so switching between on-premises and cloud models is fully transparent to upstream applications.
Security-First Architecture Design
Security is not an afterthought — it is embedded into every layer from day one. From DLP engines and LDAP authentication to tamper-proof audit logs, all validated in production deployments.
Zero Trust Architecture
LDAP/AD authentication with short-lived JWT tokens — every request verified independently. No implicit trust, regardless of network origin.
Data Sovereignty & Local Inference
AI inference runs entirely within the client's environment via vLLM. Data never leaves organizational boundaries. Fully air-gapped deployment supported; model updates delivered via secure offline media.
Built-in DLP Engine
A DLP engine scans every request before it reaches the LLM — detecting ID numbers, phone numbers, account patterns, and prompt injection attacks in real time, with automatic blocking of high-risk requests.
Tamper-Proof Audit Logs
All AI requests, data access, and system operations written to a complete audit trail with unique trace IDs — meeting government and healthcare regulatory requirements.
Three-Layer Intelligence Stack
Capture, Understand, Act: each layer operates independently while tightly integrated with the others, driving the complete inference and execution cycle of agentic systems.
Capture
Communication / Signals / DevicesThe first layer captures all raw signals — VoIP voice calls, SNMP automated device discovery, bidirectional network traffic mirroring, and sensor events. Through proprietary communication protocol platforms, data is encrypted and structured at the point of entry, providing the foundation for downstream agent inference.
- VoIP real-time voice capture & recording
- SNMP multi-vendor device discovery & fingerprinting
- Bidirectional traffic mirroring (Elasticsearch time-series storage)
- Edge preprocessing & end-to-end encryption
Understand
Knowledge Graph / AI Inference / Anomaly DetectionThe second layer transforms raw data into actionable knowledge. Ontology-driven knowledge graphs establish entity relationships; local LLMs perform semantic understanding; Isolation Forest models run real-time anomaly detection over time-series data, precisely identifying suspicious behavioral patterns.
- Graph database knowledge graph + vector semantic search (RAG)
- Local vLLM inference — data stays on-premises
- Isolation Forest network traffic anomaly detection
- Cross-system entity resolution & relationship mapping
Act
Agent Execution / Automation / DecisionsThe third layer transforms understanding into autonomous action. Agent workflows automatically generate legal document drafts, trigger alert escalation, execute energy dispatch decisions, or coordinate cross-system tasks through BullMQ background queues — completing complex multi-step operations without human intervention.
- AI agent document generation (legal briefs, reports, summaries)
- Intelligent alert tiering & auto-escalation
- Automated energy dispatch & peak-shaving decisions
- BullMQ cross-system task orchestration engine
System Integration Methodology
Site Assessment
Deep understanding of existing infrastructure, security requirements, compliance needs, and business processes — determining agent system integration scope and priorities.
Architecture Design
Designing agent system architecture based on assessment results — including LLM routing strategy, knowledge graph modeling, DLP rules, network topology, and failover mechanisms.
Incremental Deployment
Modular deployment in phases, with each stage fully tested and validated — ensuring zero impact on existing operations, with blue-green switching support.
Continuous Operations
Long-term operational support after deployment — including 24/7 monitoring, agent model updates, performance optimization, and compliance reporting.
DevOps & Long-term Operations
Deployment is just the beginning. We provide a complete long-term operations framework, ensuring agentic systems continuously evolve, receive model updates, and optimize throughout their lifecycle.
Infrastructure as Code
All infrastructure configurations managed as code, enabling version control, automated deployment, and reproducible environment builds.
Continuous Monitoring & Response
24/7 intelligent monitoring with automated alert tiering and incident response workflows, minimizing mean time to repair.
Progressive Upgrades
Zero-downtime updates through blue-green deployment and canary release strategies. Every upgrade goes through complete regression testing and validation.
Want to dive deeper into our architecture?
Our engineering team can conduct a technical deep-dive tailored to your environment — covering agentic system design, DLP configuration, and on-premises deployment planning.
Schedule a Technical Consultation