AI Agents for Corporations
Implement local AI agents — fully automate with GenAI, zero data leakage.
3–5 days Engineering teams, tech leads, CTOs
Overview
Your company’s data is its most valuable asset — and it should never leave your walls. This masterclass teaches your team to build, deploy, and operate autonomous AI agents entirely on your own infrastructure. No third-party API calls with sensitive data, no vendor lock-in.
What you’ll build
By the end of this masterclass, your team will have a working local AI agent system that can:
- Execute multi-step business workflows autonomously
- Interact with internal databases, APIs, and tools
- Run entirely on-premise or in your private cloud
- Operate within your existing security and compliance boundaries
Curriculum
Day 1 — Foundations & Local LLM Setup
- Local LLM deployment (Ollama, vLLM, llama.cpp) — choosing the right approach for your infra
- Model selection: when to use 7B vs 13B vs 70B parameter models
- Benchmarking latency, throughput, and quality for your use case
- GPU provisioning and cost modelling
Day 2 — Agent Architecture
- Agent design patterns: ReAct, plan-and-execute, reflexion
- Tool definition and function calling with local models
- Memory systems: conversation context, long-term retrieval, working memory
- Guardrails and output validation
Day 3 — Integration & Orchestration
- Connecting agents to internal systems (databases, REST APIs, file stores)
- Multi-agent collaboration patterns
- Task queuing and orchestration with Celery / background workers
- Error handling, retries, and fallback strategies
Day 4 — Security & Compliance
- PII detection and redaction pipelines
- Audit logging for every agent decision
- Role-based access control for agent capabilities
- GDPR / ISO 27001 compliance patterns for AI systems
Day 5 — Production Deployment
- Containerisation and deployment (Docker, Kubernetes)
- Monitoring: latency, token usage, failure rates, drift detection
- A/B testing agent behaviours
- Scaling strategies and cost optimisation
- Capstone: deploy your agent to a staging environment
Prerequisites
- Familiarity with Python
- Basic understanding of REST APIs
- Access to a machine with a GPU (or cloud GPU budget)
Outcomes
Your team leaves with a production-ready local AI agent, deployment playbook, and the confidence to iterate independently.
Interested in this masterclass?
Tell me about your team and I'll tailor the programme to your needs.
Book this masterclass