Enterprise‑Grade, Privacy‑Preserving, Self‑Improving Agents
Emphasizes best practices for deployment: low-latency endpoints, security, governance, and integration into enterprise workflows.
This repository lays out the design and implementation steps for building Agentic AI systems—intelligent software agents that not only respond to queries but also reason, plan, collaborate, and take actions autonomously.
This project provides a blueprint for developers who wish to go beyond conventional chatbots and build truly goal-driven AI agents while guaranteeing data sovereignty, auditability, and low‑latency orchestration.
| Capability | Description |
|---|---|
| 🧠 Goal‑Driven Agents | Multi‑step planning, tool use, and self‑reflection (tool orchestration frameworks like LangChain, AutoGen, and CrewAI) and reasoning with objectives and sub-goals. |
| 🗄️ Long‑Term Memory | Vector‑store RAG (Milvus) + episodic & semantic memory layers. |
| 🔄 Self‑Optimisation | RLHF loops, performance telemetry, and automated prompt refinement. |
| 🛡️ Enterprise Security | Zero‑Trust (Cloudflare Zero Trust Network Access (ZTNA)), SSO + MFA, Encrypted transit & at‑rest, SOC 2 alignment. |
| ☸️ Cloud‑Native Ops | Kubernetes 1.30, Helm, ArgoCD, GPU autoscaling (NVIDIA T4/A100). |
| 📊 Observability | OpenTelemetry, Prometheus + Grafana, LLM‑specific red/blue team dashboards. |
- PLANNING.md: Contains the high-level vision, architecture details, technology choices, constraints, and references for the project.
- TASK.md: Tracks current tasks, backlog items, and completed tasks. This file is frequently updated as the project evolves.
- Review PLANNING.md to understand the overall approach, architecture, and design constraints.
- Check TASK.md for the current project status, tasks in progress, and future enhancements.
- Contribute by creating pull requests or issues. Always reference PLANNING.md and update TASK.md accordingly.
Developed by an AI Engineer and Cloud/DevOps Engineer/Consultant with dual Master’s degrees in Computer Science and Data Analytics from top global universities. This project leverages real-world experience in building large-scale AI systems and integrating advanced Large Language Models in complex, enterprise-grade scenarios.