Agentic AI & LLM Engineering
Production agentic AI systems, RAG pipelines, and LLM integrations that deliver real business outcomes — not demos. Specialised in healthcare AI and enterprise automation.
The Challenge
Most AI Projects Never Make It to Production
Industry research consistently shows that over 85% of AI proofs-of-concept fail to reach production. The gap between a compelling demo and a reliable AI system running 24/7 in an enterprise environment is wider than most organisations anticipate. Agentic AI — systems where LLMs plan, reason, and take actions autonomously — closes that gap dramatically in capability, but opens a new one in complexity. An agentic system that works perfectly in testing can hallucinate in production, loop indefinitely on an unexpected input, or generate outputs that look correct but contain dangerous clinical errors. Healthcare AI is categorically different from consumer AI. A medical recommendation made by an LLM based on incomplete patient data is not a failed UX — it is a clinical risk. Every healthcare AI system I build is designed with guardrails, human escalation paths, output validation, and audit trails as first-class requirements — not afterthoughts. At Octdaily I shipped a production Agentic Medical Director AI using Claude Opus and GPT-4o that monitors 20,000+ US Skilled Nursing Facilities around the clock. It identifies quality gaps, generates QAPI recommendations, and escalates edge cases to human clinicians. It handles tens of thousands of inference requests per day without hallucination-driven clinical errors, because it was built with the right architecture — not just the right model. I also trained 100+ engineers in agentic AI development workflows using Cursor IDE and Claude, achieving a 40% improvement in feature delivery velocity. Building production AI is what I do.
Deliverables
AI Engineering Capabilities
- Agentic AI system design and implementation — multi-step task planning, tool use orchestration, memory management, and autonomous decision-making with appropriate human-in-the-loop escalation
- Claude 3 (Anthropic) API integration — Opus for complex clinical reasoning, Sonnet for balanced performance, Haiku for high-volume classification tasks — with prompt engineering optimised for each use case
- OpenAI GPT-4o and GPT-4 Turbo integration — structured output extraction, function calling, vision capabilities for medical document processing
- Azure OpenAI Service deployment — enterprise-grade LLM access with data residency, private endpoints, no data logging to OpenAI, and compliance-friendly architecture
- RAG (Retrieval-Augmented Generation) pipeline design — document ingestion, chunking strategy, embedding model selection, Azure AI Search or Pinecone vector store configuration, and retrieval quality evaluation
- LangChain and Semantic Kernel workflow orchestration — multi-agent pipelines, chain-of-thought prompting, tool integration, and agent memory across conversation turns
- Model Context Protocol (MCP) server development — connecting AI agents to internal tools, databases, APIs, and EHR systems with standardised tool definitions
- Cursor IDE agent mode training programmes — structured workshops and pair programming sessions that teach engineering teams to use AI-assisted development effectively, achieving 30–50% velocity improvements
- Healthcare AI clinical decision support — QAPI analysis, care plan recommendation, clinical note summarisation, ICD-10 coding assistance, prior authorisation support
- AI cost optimisation — prompt caching, model routing (expensive model only when needed), output caching, token budget management, and monitoring of cost-per-request
- AI observability and evaluation — tracing with LangSmith or custom logging, output quality scoring, A/B testing of prompt variants, regression testing for prompt changes
- AI safety and guardrails — input validation, output filtering, PII detection and redaction, toxicity screening, clinical claim validation, and human escalation triggers
Stack
AI Technology Stack
Process
AI Project Delivery
A clear, predictable engagement model with no surprises.
Use Case Validation & Feasibility
Before writing a line of code, we rigorously define the business problem, success metrics, and data requirements. I challenge the AI assumption — sometimes a deterministic rules engine, a traditional ML model, or a simple search system will outperform an LLM at a fraction of the cost. I will tell you honestly when AI is not the right tool.
Data Assessment & Evaluation Framework
Assess the data available for grounding (RAG corpus, tool integrations, structured context). Define an evaluation framework before building — what does a correct output look like? How do we score output quality automatically? Without an evaluation framework, you cannot measure progress or catch regressions.
Prototype & Iterate on Quality
Build a minimal prototype targeting the core use case. Run it against representative test cases. Measure quality scores against the evaluation framework. Identify failure modes — hallucination patterns, refusals, latency spikes. Iterate on prompts, retrieval, and architecture until quality meets the defined threshold.
Production Architecture Design
Design for reliability, cost, and observability — not just capability. Every LLM call has a timeout, a retry budget, and a fallback. Costs are tracked per-request. Outputs are logged with full context for debugging and audit. For healthcare, PHI handling, HIPAA compliance, and clinical safety guardrails are first-class architectural concerns.
Build, Integrate & Test
Implement the production AI pipeline. Integrate with your existing systems — EHRs, internal APIs, databases, notification channels. Adversarial testing: prompt injection attempts, boundary cases, malformed inputs, high concurrency. Every edge case documented and handled.
Deploy, Monitor & Improve Continuously
Deploy with full observability from day one. Monitor output quality, latency, cost, and error rates in real time. Review outputs weekly — capture user feedback, identify systematic failure patterns, and run controlled prompt experiments to improve quality over time. AI systems improve with use; the monitoring infrastructure makes that improvement systematic.
FAQ
Frequently Asked Questions
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