Designing autonomous AI agents: tool integration, multi-agent architecture, permission design, and dividing work between agents and people.
Explains converged database implementation patterns for unifying LLM inference logs across multiple storages, improving anomaly detection in production. Covers unified I/O, latency, token usage & error tracking.
Discover how voice AI agents automate call handling. Explore differences from traditional IVR, multilingual support, and key implementation steps and tips.
Implementation steps for integrating AI Observability into existing application monitoring infrastructure. Covers data pipeline setup, phased adoption strategies, and legacy system coexistence.
Harness AI is a suite of AI agents by DevOps platform Harness. Learn agent roles, supported models, pre-deployment checks, PoC steps, and HITL design in one practical guide.
From "adding AI" to "redesigning with AI." Learn AI-native management transition steps, ERP/FMS integration strategies, and real-world examples from Thai and Japanese companies.
Learn how EDD integrates evaluation metrics throughout the entire cycle—not gut feeling—plus practical steps to auto-tune prompts and parameters.
Discover how to design AI integration architecture across manufacturing, procurement, and logistics data silos—and solve the data quality issues blocking ROI for 89% of companies.
Least privilege for LLM tool execution: scope AI agent tool & API permissions with whitelists, read-only keys, HITL approval, and audit logs for production.
Learn patterns & implementation steps for multi-tenant cache design that securely shares prompt context across tenants, significantly reducing inference costs.
Use AI not to cut hours, but to amplify output. Data-backed strategies for lean teams—from startups to elite enterprise units—on maximizing AI leverage for peak productivity.
Learn how Non-Human Identity (NHI) management defines *who* AI agents act as. Covers why human IAM falls short, credential issuance, short-lived tokens, revocation, least privilege, and audit logging.
Evaluating AI agents requires validating tool calls and execution trajectories, not just final outputs. Learn golden set design, trajectory scoring, regression detection, and CI integration.
