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94% OF ENGINEERING ORGS USE AI. ONLY 6% SCALE IT The missing link is context and governance. That’s exactly what Atlassian is going after with its new agentic engineering stack: Code Context + governed agent loops in Atlassian Jira + DX measurement. The idea is simple: make agents a...

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Charly Wargnier ♨️

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29,622 görüntüleme • 1 gün önce •via X (Twitter)

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Charly Wargnier ♨️ profil fotoğrafı
Charly Wargnier ♨️1 gün önce

Appreciate @Atlassian giving me an early look at this one and partnering with me on the launch 🫶 There’s a lot more in the full announcement here: >

E_genius profil fotoğrafı
E_genius1 gün önce

@Atlassian @Jira the 6% stat is the real story, most teams add copilots before fixing context and evals

Charly Wargnier ♨️ profil fotoğrafı
Charly Wargnier ♨️1 gün önce

@Atlassian @Jira so true @yevloop!

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John1 gün önce

@Atlassian @Jira The Jira agent-loop piece is the interesting bit. Most agentic rollouts stall from no audit trail when something breaks, not bad models. Does the governance layer block merges or just log after the fact?

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kartik bhardwaj1 gün önce

@Atlassian @Jira The 64 percent has a selection problem in it. Teams that can hand an agent rich context are the ones with clean boundaries and current docs already, and those teams were shipping faster before any of this arrived.

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Jules SEO Whisperer1 gün önce

@Atlassian @Jira Context is the real bottleneck. Too many teams jump straight to tools without grounding agents in their actual codebase, then wonder why the output is noise. That 64% stat doesn't surprise me.

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L 🍂1 gün önce

@Atlassian @Jira Looking to expand your reach and visibility? Let’s connect

Charly Wargnier ♨️ profil fotoğrafı
Charly Wargnier ♨️1 gün önce

@Atlassian @Jira Why not! just followed and DM' ya :)

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BitFuturist1 gün önce

@Atlassian @Jira 😎

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Ali AlphaQ1 gün önce

@Atlassian @Jira Fascinating signal. I think context and governance are the real unlock here. What guardrails keep agent loops reliable at scale?

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CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.
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CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

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Anthropic engineer: “80% of our engineers are using Loops. Now everyone is exploring how to build right Context for agents.” in 3-6 months, we’ll all become context engineers. Context is the most important part of agents.” in this 1-hour talk, Anthropic engineer shows how to build a self-improving agents from sratch. It’s worth more than a $500 agentic course. Watch this video, then read the article below on how to become a context architect.

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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
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Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below

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Google just dropped a 1-hour course on how to become an AI engineer in 2026: • 00:00 - Context engineering • 10:00 - How to build, deploy, and run AI agents • 31:52 - Agentic loops, long-running AI agents • 43:22 - Building MCP from scratch • 51:20 - Prompt engineering • 59:10 - AI development trends This 1-hour watch will replace a $500 AI engineering course. Watch it today, then read the full pipeline in the article below.

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Creator of Claude Code: “I’m not prompting my agents anymore. I’m building "graphs" and "loops" - they’re building my agent. in 3-6 months, we’ll all be doing graph engineering for our agentic systems.” in 10-minute talk, Boris Cherny revealed what the future of agentic engineering will look like. worth more than a $500 agentic AI course. Watch it today, then read the full roadmap in the article below.

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Google just dropped a 1-hour course on how to become an AI engineer in 2026: • 00:00 - Context engineering • 10:00 - How to build, deploy, and run AI agents • 31:52 - Agentic loops, long-running AI agents • 43:22 - Building MCP from scratch • 51:20 - Prompt engineering • 59:10 - AI development trends This 1-hour watch will replace a $500 AI engineering course. Watch it today, then read the article below.

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Anthropic engineer just released a 2-hour workshop on "Graph Engineering" for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to RAG & Graphs • 06:39 - Core of "Graph Engineering" (state, nodes) • 14:29 - 3 feedback loops of Graph agents • 23:06 - Agent evaluation with Graphs • 36:29 - Agent cycles in graphs • 1:15:22 - Agentic RAG & agent context • 1:41:20 - Evaluation datasets based on Graphs This 2-hour workshop will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a Graph Engineer in the article below.

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AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with Qodo and taught by Nnenna 👩🏽‍💻✨, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free:

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