Agentic AI Cost Optimization: LLM API Budgeting and Token Management
Reduce agentic AI costs by 50-80% with token budgeting, model routing, prompt caching, response truncation, batch processing, and cost monitoring.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Reduce agentic AI costs by 50-80% with token budgeting, model routing, prompt caching, response truncation, batch processing, and cost monitoring.
Explore how to build agentic AI data pipelines that combine traditional ETL with LLM-powered extraction, classification, and validation loops.
Design PostgreSQL schemas for agentic AI systems covering conversation storage, agent state machines, tool logs, and vector memory columns.
Step-by-step guide to setting up your agentic AI dev environment — VS Code extensions, Docker Compose for LLM services, GPU passthrough, and debugging config.
Master the full agentic AI development lifecycle from ideation to monitoring. A phase-by-phase roadmap with tech stack choices, team structures, and pitfalls.
Comprehensive guide to the 2026 agentic AI tech stack — LLM providers, agent frameworks, vector DBs, observability, and deployment infrastructure compared.
Advanced architectural patterns for agentic AI — event sourcing for agent actions, CQRS for state management, and saga pattern for multi-agent workflows.
Production patterns for agentic AI backends with FastAPI — WebSocket streaming, background agent tasks, dependency injection, and Pydantic models for agents.
Compare NATS, Kafka, and RabbitMQ for agentic AI workloads. Learn async tool execution, event-driven agents, and dead letter queue patterns.