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Building the memory layer for the hyper-agent generation. Governed shared memory for AI agent fleets. Open source and https://nitter.cf/t.co/z92PMs8e9a

Global
Joined September 2025
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We gave a Databricks Agent Bricks agent and a Claude agent one shared memory over MCP. Told Databricks about a trip. Claude planned dinner around it, then corrected the date. A brand-new Databricks session already knew. ~530 lines. Full code 👇 caura.ai/use-cases/databrick…
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Meta Muse says it plays with the big guys. The big guys aren't impressed. 8 frontier models graded each other blind, 11,137 times. Muse Wonder debuted 6th. Claude Opus 5.5 pulled away: a 126-point Elo lead, up from 53 last week. caura.ai/blog/meta-muse-debu…
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We had 6 frontier models grade each other's work 3,570 times. They inflated their own scores (GPT-6 Astra: +0.76). They gave famous names a bump (up to +0.25). They favored whoever answered first. And the harshest judge finished #1. AIs have egos → caura.ai/blog/ais-have-egos/
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2/ Ego. 5 of 6 models rated their own answers above what their peers gave them. GPT-6 Astra: +0.76 on a 10-point scale. That's "good" → "great" based on authorship alone. Only Opus 5.5 (+0.07) and Grok 4.7 (−0.02) judged themselves honestly.
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4/ Family resemblance: Opus 5.5 and Fable 5.1 agreed at r = 0.89. Every cross-lab pair: 0.56–0.80. The twist: Opus was the toughest grader (avg 7.50) and still finished #1. The most generous judge finished 5th.
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5/ If you let AI judge AI: • Never let a model grade itself • Hide the names • Shuffle the order • Mix judges from different labs There's no single best model, only the best one for the task, this month. Swap the model. Keep the memory.
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Caura scores 92.2% on LongMemEval_S: 461/500 under the benchmark's reference judge. One config for all six question types. No dataset labels. Gemini 3.8 Flash answering. 22k tokens of context per question. Harness, contexts and verdicts are all public. caura.ai/blog/caura-longmeme…
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We counted every tool grant in a 5-agent fleet: 14 of 60 possible agent–tool pairs. Prompt-only enforcement: 0. That's the whole lesson of multi-agent coordination. A decision one agent makes has to become a constraint the next agent can't route around. caura.ai/blog/multi-agent-sy…
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Harness Engineering: Stop Asking Your Agents to Decide What to Remember medium.com/caura-ai/harness-…
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Time to Stop Asking Your Agents to Decide What to Remember Deterministic memory: why the harness — not the model — should own every read and write. With a 130-line cross-vendor demo: a Gemini agent writes, a Claude agent recalls. caura.ai/blog/harness-engine…
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just published How AI Agents Share Knowledge — and Learn From Each Other’s Mistakes medium.com/caura-ai/how-ai-a…
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Just published What Is Agent Fleet Memory? medium.com/caura-ai/what-is-…
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We don't have a marketing team. We have a marketing fleet. Five agents. Twelve live data sources. One governed memory they all read and write. This is what digital labor looks like when it stops being a slide ↓
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Ten rules for AI agents that survive production — from running our own marketing console: ten data sources, twenty-eight tools, one governed brain. Written after the demos stopped being the hard part ↓
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One of your agents figures something out at 2am. By morning it's gone — and the next agent repeats the same mistake, burns the same three hours, and nobody notices. That's not a memory problem. It's a teamwork problem. It starts with one memory that survives the night — 5 minutes: memclaw.net/docs/tutorials/f…
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