AI for Software Architects

The canonical patterns of AI system design — with rigorous trade-off analysis and failure mode documentation for each. Retrieval-Augmented Generation (RAG): architecture variants (naive, advanced, modular, agentic), component-level trade-offs, and the 12 ways naive RAG fails in production. The agent loop pattern: planning, tool-use, reflection, and self-correction — when agents are appropriate and when they are catastrophically inappropriate. Tool-use and function calling patterns: designing reliable tool schemas, handling tool failures, preventing runaway execution. Chain-of-thought and reasoning patterns: when explicit reasoning improves output quality vs when it just adds cost. Multi-agent orchestration: orchestrator-worker, peer-to-peer, and market-based coordination — failure mode analysis for each topology. Hybrid AI/traditional architecture: designing the boundary between AI judgment and deterministic code. Context management architecture: windowing strategies, compression, summarisation, and the fundamental context-length constraint.

Most organisations choose their LLM provider based on marketing or the loudest engineer. This module teaches a systematic, defensible evaluation methodology. Evaluation framework design: defining the dimensions that matter for your specific use case — accuracy, latency, cost, data residency, API reliability, and model stability. Benchmark design: building task-specific evaluation datasets that reflect your actual use case rather than public benchmarks. Avoiding benchmark gaming: why MMLU, HumanEval, and similar are insufficient for production decisions. Latency measurement: p50/p95/p99, time-to-first-token, tokens-per-second — what each measures and which matters. Cost modelling: total cost of ownership including API, infrastructure, latency-induced server costs, and human evaluation. Data residency and sovereignty: where inference happens, what data logging vendors perform. Model stability risk: what happens when a vendor silently updates the model. Multi-model architecture: designing systems that can switch providers without re-engineering. Open-source vs proprietary: a framework for evaluating when self-hosting is worth the operational overhead.

AI systems have different scalability characteristics from traditional software — inference is expensive, stateful in complex ways, and subject to quality degradation under load. Caching architecture: semantic caching (similarity-based), prompt caching (Anthropic/OpenAI prefix caching), and cache invalidation strategies. Async processing: decoupling user-facing latency from AI inference latency using message queues and background processing. Load balancing inference: distributing requests across multiple model endpoints, provider routing, and failover. Rate limiting and backpressure: protecting downstream AI APIs from overload. Multi-region and multi-provider architecture: systems that continue operating through provider outages. Cost modelling at scale: building models for 10K, 100K, and 1M users — identifying dominant cost drivers and available levers. Quality vs cost routing: cheap models for simple tasks, expensive models for complex tasks. Reliability patterns: circuit breakers, retries with exponential backoff, timeouts, and graceful degradation.

Security for AI systems requires a different threat model from traditional application security — with new attack vectors and architectural defences that must be designed in from the start. AI threat modelling: adapting STRIDE and PASTA methodologies to AI systems. AI-specific threats: prompt injection, indirect injection via data sources, jailbreaking, model extraction, training data poisoning, and membership inference attacks. Prompt injection architecture: defence-in-depth including input sanitisation, instruction hierarchy, output validation, and sandboxed execution. Indirect prompt injection: the unique RAG vulnerability where retrieved documents contain malicious instructions — architectural mitigations. Data poisoning defence: validating and sanitising content before ingestion into RAG vector stores. Model extraction and IP protection: controls preventing proprietary prompt reconstruction. PII architecture: data classification, PII detection at ingestion and output, tokenisation, and differential privacy. Excessive agency architecture: least-privilege for AI agents — capability minimisation, confirmation requirements, reversibility, scope constraints. Security review process: conducting AI architecture security reviews.

AI governance is increasingly a legal and regulatory requirement — and the architectural decisions made at design time determine whether governance is effective or cosmetic. Explainability architecture: designing systems that produce genuinely useful explanations for regulators, end users, affected individuals, and technical reviewers — local (SHAP, LIME), global (model cards), and contrastive explanations. Audit trail architecture: what to log, tamper-proof log storage (cryptographic chaining, append-only stores), retention policies, and query interfaces for auditors. Bias monitoring infrastructure: data pipelines and statistical tests for detecting demographic bias in production decisions over time. Human override architecture: systems where humans can always review, override, and correct AI decisions — with overrides logged and analysed. Model risk management architecture: model inventory, validation workflows, ongoing monitoring, and retirement processes. Privacy-preserving AI: differential privacy, federated learning, and on-device inference for privacy-sensitive use cases.

Multi-agent AI systems are increasingly the architecture of choice for complex enterprise automation — but notoriously difficult to make reliable. Multi-agent topologies: orchestrator-worker, peer-to-peer, hierarchical, and market-based coordination — trade-offs and failure mode analysis for each. Failure isolation: designing systems so one failing agent doesn't cascade into total failure. Circuit breakers, bulkheads, and timeout patterns for agent interactions. State management: shared vs distributed state, consistency guarantees, and handling state corruption mid-task. Idempotency in agent systems: designing tools and state mutations to be safely retried. Human-in-the-loop architecture: approval gates, escalation paths, and override mechanisms that don't bottleneck the system. Audit trail design for multi-agent systems: logging reasoning chains, tool calls, and state transitions in a format that is both comprehensive and interpretable. Testing multi-agent systems: simulation environments, chaos engineering, and evaluation frameworks for complex agentic behaviour.

The most technically correct architecture fails if it cannot be communicated and defended. This module covers communication skills and frameworks for AI architectural decisions. Architecture Decision Records (ADRs) for AI systems: structure, content, and the discipline of documenting decisions and rejected alternatives. Communicating to technical peers: architecture review boards, RFC processes, and peer review culture. Communicating to non-technical executives: translating architectural choices into business risk, cost, and competitive advantage. Communicating to regulators and auditors: the specific format, evidence, and language regulatory audiences expect. Managing architectural disagreement: how to reach and implement decisions when senior engineers disagree. Presenting AI risk: risk quantification, probability estimates, and the responsibility to communicate uncertainty clearly. Building technical strategy documents: evidence-based AI technology strategy that influences investment decisions.

The capstone is a high-fidelity simulation of architectural leadership — designing a complete AI system, defending it through rigorous review, and demonstrating the communication and governance skills that distinguish principal engineers. Each participant (or pair) receives a complex AI system brief and must produce: a full architecture design with component diagrams and data flow; an ADR documenting key decisions and rejected alternatives; a security architecture and threat model; a governance framework appropriate for the industry context; a scalability and cost model; a vendor evaluation and selection recommendation; and a live presentation at an architecture review with challenging Q&A. The review panel consists of HYVE principal engineers and, for Elite cohort participants, invited external architects from the engineering community.

Learning Outcomes

• ✓Design and evaluate AI system architectures using canonical patterns — RAG, agent loops, multi-agent orchestration, tool-use, and hybrid AI/traditional systems — with documented trade-offs and failure modes for each
• ✓Run rigorous, vendor-independent LLM evaluation: benchmark design, evaluation dataset construction, latency measurement, cost modelling, and data residency assessment — producing an objective, defensible model selection recommendation
• ✓Architect AI systems for scalability, reliability, and cost efficiency: semantic caching, async processing, multi-provider routing, load balancing inference, and cost modelling at 10K, 100K, and 1M users
• ✓Build AI security architecture: threat modelling for AI systems, defence-in-depth for prompt injection, data poisoning, model extraction, PII leakage, excessive agency, and OWASP LLM Top 10 mitigations at the architectural level
• ✓Design AI governance frameworks for regulated industries: explainability architecture, bias monitoring infrastructure, tamper-proof audit trail design, human override systems, and model risk management processes
• ✓Design and evaluate multi-agent system architectures: orchestration topologies, failure isolation, idempotency, state management, and the architectural constraints that make multi-agent systems reliable
• ✓Lead AI architectural reviews with confidence — presenting recommendations, managing stakeholder disagreement, and communicating technical risk clearly to non-technical audiences
• ✓Evaluate AI vendors, open-source models, and third-party AI services with a structured methodology — producing investment recommendations accounting for capability, operational risk, vendor lock-in, and total cost of ownership

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