Designing autonomous AI agents: tool integration, multi-agent architecture, permission design, and dividing work between agents and people.
Eliminate the biggest barrier to production-ready autonomous agents: lack of governance. Step-by-step guide to preventing agent drift and designing accountability.
Most AI agent failures stem from data, not models. Learn how to run a data readiness audit—evaluating quality, accessibility, and structure of internal data.
Learn how "token traps" cause billing spikes in high-frequency agent loops, and how to prevent cost explosions with budget caps, throttling, and smart loop design.
Explore how "thinking time" causes response delays in multi-step reasoning agents, and compare latency budget allocation strategies and implementation patterns based on task complexity.
From prompting to designing "loops" where AI works autonomously. We explain Loop Engineering—its meaning, evolution, components, and implementation considerations.
Learn design patterns and implementation steps for circuit breakers and kill switches that automatically detect runaway costs and hallucinations for immediate shutdown.
Learn what an AI-Ready data foundation means, how it differs from DWH and RAG, and the 3 layers and 5 requirements explained in a vendor-neutral way.
Detect shadow AI (ChatGPT, Gemini) used without approval, and learn the audit process covering risk assessment, policy development, and PDPA compliance.
Learn how to design coordinated multi-agent systems. Covers role allocation, communication design, and error handling with concrete architecture diagrams.
Democratizing AI agents: business teams building agents in-house with no-code/low-code. Learn citizen development, governance, permission design, and how to embed internal development.
Learn how AI agents maintain long-term memory across extended tasks and multi-session workflows. Covers memory store selection, read/write flows, freshness management, and memory poisoning defenses.
Producing AI agents requires embedding operational costs and economic models into the design layer—not just reducing tokens. B2B design principles for 2026 explained.
