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Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination....

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Jurij Skornik

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11,802 Aufrufe • vor 7 Monaten •via X (Twitter)

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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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Lauren Tan is shipping 1000 of PRs a month with AI agents Matt Pocock just sat down with her to unpack how the system actually works: · 04:35 - how Lauren got to thousands of PRs without drowning in AI slop · 13:20 - why pstack starts with skills, not bigger prompts · 24:10 - verification: make the agent prove its work before you trust it · 36:45 - redesign the codebase so agents naturally take the right path · 49:30 - how to turn one coding agent into a high-velocity software factory the biggest lesson isn’t “run more agents.” it’s making one agent trustworthy enough that you can safely run 10, 20, then hundreds of tasks in parallel. skills → constraints → verification → cloud agents → parallel execution Lauren basically treats the codebase as infrastructure for the agents themselves. Humans decide what matters. Agents do the work, prove it works, and keep the loop moving. Probably one of the best conversations on agentic engineering right now. watch it before you add another 20-page prompt to your coding agent.
1:03:08

Lauren Tan is shipping 1000 of PRs a month with AI agents Matt Pocock just sat down with her to unpack how the system actually works: · 04:35 - how Lauren got to thousands of PRs without drowning in AI slop · 13:20 - why pstack starts with skills, not bigger prompts · 24:10 - verification: make the agent prove its work before you trust it · 36:45 - redesign the codebase so agents naturally take the right path · 49:30 - how to turn one coding agent into a high-velocity software factory the biggest lesson isn’t “run more agents.” it’s making one agent trustworthy enough that you can safely run 10, 20, then hundreds of tasks in parallel. skills → constraints → verification → cloud agents → parallel execution Lauren basically treats the codebase as infrastructure for the agents themselves. Humans decide what matters. Agents do the work, prove it works, and keep the loop moving. Probably one of the best conversations on agentic engineering right now. watch it before you add another 20-page prompt to your coding agent.

Morlex

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