| Benchmark | What it measures | SOTA as of June 2026 | |-----------|----------------|----------------------| | | Real-world coding agents | 72% (OpenDevin) | | AgentBench | Multi-environment tasks | 68.5 (GPT-5-mini) | | WebArena | Web navigation | 52.3 (AutoWebAgent) | | ToolEmu | Tool use safety | Claude-4: 94% safe | | MetaTool | Tool selection accuracy | GPT-5: 91% | Updated PDF note : Download the latest leaderboard CSV from PapersWithCode or Hugging Face’s leaderboards space. Part 6: Practical Tutorial – Build a Research Agent (From Scratch) Here’s a minimal LangGraph agent (copy-paste into a .py file and run). This is the “Ur-text” of agentic AI.
Save this as agentic_bible_example.py . Run it with your OpenAI API key. That’s your first agent. Q1: Is there actually a PDF called “The Agentic AI Bible”? A: No official one. The term is used by the community to refer to a collection of best practices. This article + the linked framework docs = your bible.
class AgentState(TypedDict): query: str research_notes: List[str] iteration: int the agentic ai bible pdf upd
A: “Building LLM Agents” by O’Reilly (2025), “Hands-On Agentic AI” (Packt, 2026). But both are outdated within months. Use framework docs + ArXiv.
output = app.invoke("query": "Latest advances in agentic AI memory systems", "research_notes": [], "iteration": 0) print(output["research_notes"]) | Benchmark | What it measures | SOTA
llm = ChatOpenAI(model="gpt-4o") search = TavilySearchResults(max_results=3)
def research_node(state: AgentState): query = state["query"] results = search.invoke(query) notes = [r["content"] for r in results] return "research_notes": notes, "iteration": state["iteration"]+1 Save this as agentic_bible_example
That curated collection, updated quarterly, is the real “Agentic AI Bible.”