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@SentientAGI educator week 18: recursive intelligence in action
when we talk about “recursive intelligence”, we’re referring to a system that doesn’t just give answers but instead breaks problems down, delegates the parts and weaves the results back together.
tbis is crucial because single models can surely do a lot, but real-world problems are rarely solved in one go. what you need is decomposition, specialization and a clear audit trail.
sentient roma is a meta-agent framework where a top-level agent takes a request, breaks it into smaller tasks, assigns each to the right agent and then merges the results into a final output.
example: a user says, “audit my defi portfolio, check contracts and suggest safer allocations”
1. parse & plan: the meta-agent splits the job into three tasks - gather on-chain data, scan smart contracts and optimize the portfolio.
2. spawn & route: each task goes to a specialist agent - the data agent pulls from defillama and coingecko, the security agent runs checks and the optimizer runs simulations.
3. run in parallel: all tasks run at once, cutting latency and using the best tool for each job.
4. aggregate & verify: a coordinator combines the results, checks for inconsistencies and triggers rechecks if needed.
5. synthesize & explain: a narrative agent crafts the final answer and every step is logged so you can trace the reasoning.
why recursive setups are better:
• accuracy - each step uses domain-expert tools
• speed - parallel tasks finish faster than one big pass
• traceability - every reasoning chain is visible
• modularity - agents can be swapped without retraining the system
on token-level routing: instead of one model handling all tokens, the system decides which model handles which part. strong reasoning models handle the logic, while lighter models handle tone. this result into sharp facts, natural voice, efficient compute.
safety and auditability aren’t optional either. recursive systems log every agent call so you can:
- verify data sources
- replay agent steps
- run provenance checks for sensitive operations
where to tread carefully:
• coordination overhead - orchestration must stay efficient
• failure modes - backups for specialist errors
• security surface - more agents = more to secure
the grid ties it all together, routing queries across a distributed network so roma can pick the best agent and let builders plug in new ones seamlessly.
in short: recursive intelligence turns ai from a single-response system into a reliable, auditable team of specialists. it’s how you scale trust, accuracy and depth for complex real-world problems.