Reusable patterns from 2 years of production AI systems.
These are not theoretical patterns. Each one emerged from a real production failure or a painful debugging session. We extracted them, documented them, and now use them as defaults in every new system we build.
Eight patterns we use in every system.
Click any pattern to expand the full documentation: when to use it, when to avoid it, and a TypeScript pseudocode implementation.
Standardised interface for agent tool calls with retry logic and structured error handling.
Working, episodic, and semantic memory abstraction that keeps agents context-aware without stuffing prompts.
Typed, versioned prompt templates with variable injection, version pinning, and A/B evaluation support.
Orchestrates task distribution across multiple specialised agents with dependency resolution and result aggregation.
Structured output validation with automatic retry on schema violation and error injection into the retry prompt.
Exponential backoff, configurable fallback chains, and automatic human escalation when all recovery paths are exhausted.
Intercepts and logs every LLM request and response with full context: cost, latency, model version, and validation result.
Per-task token budget enforcement that prevents runaway agent loops from incurring unexpected API costs.
Use these in your project.
When we build your system, these patterns are defaults — not optional add-ons. Every production AI pipeline we deliver includes observability, output validation, failure recovery, and human review gates from day one.
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