Agentic AI Engineering 2026 Edition

Compare models by quality, cost, speed, and privacy to choose the right fit for your task.

Work with different LLM APIs and understand the trade-offs between hosted and self-hosted models.

Practice prompt engineering and structured outputs to make model responses more useful and consistent.

Build agents without frameworks to understand how tools, memory, and execution loops work.

Connect agents to tools and MCP servers, then explore frameworks and multi-agent design patterns.

Build a code review agent and add human oversight for decisions that need approval.

Understand embeddings and vector search, and prepare your data for retrieval with chunking and indexing.

Build RAG pipelines with Qdrant, then improve retrieval with filtering, re-ranking, and advanced RAG techniques.

Manage context and memory so your application can use relevant information across multi-turn conversations.

Define quality criteria and evaluate individual components, agents, and complete AI workflows.

Write automated project tests, investigate failures, and improve error handling.

Use monitoring and human feedback to find weak spots and measure whether your changes improve results.

Deploy applications with FastAPI and Docker, and use self-hosted models to keep sensitive data within your infrastructure.

Apply PII detection, guardrails, and prompt-injection defenses to protect data and control agent behavior.

Improve production reliability and cost with caching, model routing, monitoring, and robust failure handling.

Bring models, data, and application logic together in an AI product that addresses a practical problem.

Test your product, evaluate its outputs, and explain your choices around quality, security, and cost.

Present a working demo and document your project so you can show what you built and how it works.