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IBM just released a 1-hour course on building agentic knowledge graphs from scratch: • 00:00 - Introduction to knowledge graphs • 05:35 - Building your first agentic graph • 19:59 - Agentic memory powered by graphs • 30:39 - Graphs for multi-agent orchestration This 1-hour watch will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a knowledge graph engineer in the article below.

Introducing Claude Opus 5. It's a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price.

Introducing Claude Opus 5. It's a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price.

HERE WE GO: OPUS 5 And it outperforms Fable 5 almost on all evals!! Holy cow!!

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.

A senior Anthropic engineer just dropped 12-page PDF on "Graph Engineering" for multi-agentic systems. The shift: your agents memory dies with their context window. A knowledge graph makes it permanent. Extract → Resolve → Assemble → Query → Repeat Every agentic graph has 5 stages: • Extract: Haiku pulls entities and S-P-O triples. One call per doc. The Pydantic schema is the only training data. • Resolve: Sonnet clusters "Edwin Aldrin" → "Buzz Aldrin" - zero string overlap - using descriptions as context. • Assemble: canonical nodes, typed edges, provenance on every triple. One connected graph. • Query: serialize a subgraph → Sonnet reasons over triples → every answer cites a specific edge. Plug this into multi-agent systems as shared memory. Workers write to it, evaluators fact-check against it, loops persist overnight with it. This 12-page PDF changed how I'm building multi-agent systems today. Read it now, then explore the article below.