Key Concepts
Context Engineering
Generic AI assistants hallucinate outdated syntax or invented plugin properties because they're frozen at a training cutoff. Supplying current docs before generation is what makes output trustworthy.
AI Copilot
Generates flow scaffolding from natural language, grounded in the plugin docs for your installed Kestra version. The "5% Rule": Copilot handles the structure, you tweak credentials and edge cases.
RAG — Static & Web Search
Two-phase grounding: ingest documents into embeddings once (KV Store), or retrieve live web results at query time (Tavily). Either way, the LLM answers from real content instead of memorized guesses.
Agents
AIAgent runs an agentic loop: call the LLM → execute any requested tool → feed results back → repeat until done. You specify the goal; the agent decides how — including when to stop.
Multi-Agent Systems
One agent treats another AIAgent as a tool. An analyst delegates web research to a specialist agent, keeping each agent's responsibility narrow and easier to debug.
Best Practices
Deterministic, repeatable workflows still win for compliance-critical steps — agents are for genuinely dynamic decisions. Track token cost, inject keys via secret(), rotate every ~90 days.
Single Agent vs Multi-Agent
Single Agent
Goal
↓
LLM ──→ Tool
↑ ↓
results ←───┘
↓
Answer
Multi-Agent
Goal
↓
Analyst Agent
↓ ↑
delegates synthesizes
↓ ↑
Research Agent (tool)
↓
Structured Report