How CHORUS, BUBBLE, and SLURP Anticipate a New Era of Reusable Thought
By Anthony Rawlins
CEO & Founder, CHORUS Services
Topics
Identity-first design, defense-in-depth for indirect prompt injection, and audit trails that survive an investigation.
Naïve multi-agent debate often reduces accuracy. Replace persuasion with artifact-centric critique and verification.
Naïve multi-agent debate often reduces accuracy. Replace persuasion with artifact-centric critique and verification.
Cloud + on-prem, small + large models, static + hierarchical context — the future isn’t either/or, it’s hybrid.
Deeper insights and analysis
Real-world problems demand multi-agent systems that share context, divide labor, and reason together.
Bigger models don’t solve everything. True breakthroughs will come from structure, orchestration, and hybrid intelligence.
Search isn’t just about retrieval — it’s about organizing threads of meaning. CHORUS is developing a project to rethink how agents discover context.
AI needs more than static snapshots. Decisions, justifications, and reasoning threads should be preserved over time.
If your business strategy is in the cloud, it’s not really yours. Privacy and sovereignty are shaping the future of AI infrastructure.
The future isn’t just about bigger LLMs — small, specialized models are proving more efficient and more practical.
Raw retrieval is messy. Agents need curated, layered inputs that cut through noise and preserve meaning.
Version control transformed code, but commits and diffs can’t capture how reasoning evolves. AI needs a different model of history.
Bigger context windows don’t mean better reasoning — here’s why temporal and structural memory matter more.
Neural-Symbolic AI: Bridging Intuition and Logic
Moving beyond AI replacement to create systems where artificial and human intelligence complement each other for enhanced problem-solving.
Exploring why linking knowledge is just one dimension of reasoning—and how multi-layered evidence and decision-tracking systems like BUBBLE can complete the picture.
Real-world problems demand multi-agent systems that share context, divide labor, and reason together.
Bigger models don’t solve everything. True breakthroughs will come from structure, orchestration, and hybrid intelligence.
Search isn’t just about retrieval — it’s about organizing threads of meaning. CHORUS is developing a project to rethink how agents discover context.
AI needs more than static snapshots. Decisions, justifications, and reasoning threads should be preserved over time.
If your business strategy is in the cloud, it’s not really yours. Privacy and sovereignty are shaping the future of AI infrastructure.
The future isn’t just about bigger LLMs — small, specialized models are proving more efficient and more practical.
Raw retrieval is messy. Agents need curated, layered inputs that cut through noise and preserve meaning.
Version control transformed code, but commits and diffs can’t capture how reasoning evolves. AI needs a different model of history.