The AI Operator AI Index
The AI Operator publishes practical intelligence for builders working with agentic AI, LLM workflows, automation systems, and production AI operations.
Core topics
- Agentic AIMulti-step agents, tool use, orchestration, memory, planning, evaluation
- AI AutomationWorkflow automation, business process agents, no-code/low-code integrations
- LLM SystemsRAG, prompt architecture, model routing, evals, observability
- AI ProductivityPersonal operating systems, research workflows, coding assistants
- Production AIGuardrails, cost controls, latency, deployment, monitoring
- AI x FinanceDeFi intelligence, market agents, risk-aware automation
Best starting points
- Public Chatbot GuardrailsFlagship blueprint edition for securing public AI apps
- Article archiveFull essay index with playbooks and case logs
- The HubImplementation blueprints and starter packs
- AI IndexMachine-readable content map for AI crawlers
Featured blueprints
- Public Chatbot Guardrails
Rate limits, spend caps, and input validation before you expose an LLM to the public internet.
- Agentic Operating Systems
Orchestration patterns for multi-step agents with memory, planning, and human gates.
- AI Evaluation Workflows
Model-risk interfaces and eval gates before probabilistic steps hit regulated workflows.
- RAG System Design
Tenant isolation, freshness contracts, and logging for retrieval-augmented production systems.
- AI Coding Workflows
Verify-first harnesses for agentic IDEs—rules, tests, and human checkpoints before tool sprawl.
- AI Business Operations
Inference caps, kill switches, and spend telemetry when copilots touch revenue workflows.
Author
Souriya Khaosanga — Built by Souriya Khaosanga, software engineer, AI developer, DeFi systems builder, and founder of Chainable.co. Focus areas include agentic AI, automation architecture, LLM workflows, blockchain systems, and production-grade AI implementation.
Key definitions
Terms we use
Concise definitions for generative search and new readers.
- Agentic AI
- AI systems that can reason through multi-step tasks, use tools, maintain context, and take structured actions toward a goal.
- The AI Operator
- Practical agentic AI intelligence: systems that help people research, build, automate, evaluate, and ship real workflows.
- Operator-grade AI
- AI that is useful beyond demos: observable, repeatable, cost-aware, secure, and measurable.