About the course
Bridge the gap between "vibe-coding" and production-grade agentic systems. This workshop moves beyond basic prompt engineering to focus on the architectural patterns required for truly autonomous software agents. You will learn to implement Ralph Loops - a high-reliability iteration pattern where AI agents continuously attempt tasks, receive external validation, and reflect on failures until a verifiable success state is achieved (Koning et al., 2017).
By focusing on "context fluency" and structured task delegation, this course prepares Tech Leads and Senior Engineers to build self-healing AI pipelines. We explore how to move from one-shot prompting to persistent informational environments, ensuring that your agents can make aligned micro-decisions without constant human intervention (Huntley, 2026).
Instructor-led online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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By the end of this course, attendees will be able to:
- Architect robust agentic workflows using the Ralph Loop pattern for continuous task refinement.
- Develop "context fluency" to create machine-legible informational environments that reduce rework.
- Implement adversarial evaluator agents to diagnose failure modes and score performance.
- Mitigate "design fixation" in AI systems by integrating metacognitive co-regulation loops.
- Apply the Model Context Protocol (MCP) to build extensible, tool-enabled AI servers.
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This workshop is designed for Software Engineers, Tech Leads, and AI Architects who have moved past basic LLM chat interfaces and are now building integrated agentic systems. It is particularly relevant for those responsible for the reliability and scalability of AI-driven features in production environments.
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Attendees should ideally have
Proficiency in Python or TypeScript (or similar).
Practical experience with LLM orchestration frameworks like LangGraph, CrewAI, or PydanticAI.
Basic understanding of unit testing and CI/CD pipelines.
Familiarity with prompt engineering fundamentals (chain-of-thought, few-shotting).
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This Ralph Loops course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Ralph Loop Fundamentals and Agentic Iteration
Defining the Ralph Loop: Moving from OODA loops to iterative task delegation.
The mechanics of the "Ralph Wiggum Loop": External validation vs. internal agent judgment.
Building the Harness Evolution Loop: Worker, Evaluator, and Evolution agent roles.
Identifying and preventing design fixation in reflective agents.
Context Fluency and Informational Architecture
Decomposition: Breaking complex engineering problems into parallelizable tasks.
Domain Encoding: Externalizing tacit knowledge into explicit, machine-legible forms.
Building persistent informational environments vs. transient one-shot prompts.
Constraint Definition: Scope management as a first-class concern in agent execution.
Advanced Orchestration and Infrastructure
Implementing Model Context Protocol (MCP) for autonomous tool use.
Orchestration logic: Subagent spawning, handoffs, and model routing.
Self-Regulation Loops (SRL) vs. Co-Regulation Design Agentic Loops (CRDAL).
Evaluating agentic performance: Metrics for success, latency, and token efficiency.
Productionizing Agentic Workflows
Integrating AI loops into the Scalable Agile Framework for Execution in AI (SAFE-AI).
Adversarial testing: Using LLMs to find edge cases in your own agent harnesses.
Security and observability: Monitoring agent state and preventing "infinite loops."
Case studies in engineering design: From battery configuration to autonomous code review.
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