ANA Swarm 
Turning foundation models into a coordinated swarm

powered by a shared Mindset, swarm-DMN, 

and vector-native exchange.

Do you know how your AI agent works?

When you send the second message in a chat, there’s a storm of hidden data flowing in and out. 

It burns tokens, adds latency, and quietly erodes quality.


 The Problem 🚧

Long Chats Lose Context and Common Sense


Most LLMs/GenAI systems were built for single‑request → single‑response. 

For anything richer, you need layers on top: orchestration, multi‑agent collaboration, and a purpose‑aware application layer.

Also: Costs a lot.


The Solution 🧠

Mimicking How Humans Manage It


We keep short‑term memory and context awareness without replaying every word or self‑summarizing aloud.

We detect topic shifts and think in a mindset appropriate to the moment—you don’t have to open a new “chat” with a person.

Introducing ANA: Adaptive Neuro‑Affective model‑of‑models orchestration framework 

..that coordinates foundation models and tools using:

  • MAS (Modulation & Arbitration Service): Global controller for focus vs explore, safety, verbosity, and cost.
  • STSM (Short‑Term Shared Memory): Compact, auditable memory between turns via Context Capsules and Mindset.
  • Vectors & IDs, not blobs: Agents pass IDs to common objects (policy, persona, templates) instead of re‑serializing long text.

What ANA Feels Like

  • Short‑term memory that actually helps, not a thousand‑token replay.
  • A general Mindset (urgency, confidence, focus) that adapts decoding and retrieval.
  • Dynamic context about the conversation and common objects (e.g., “neighbor,” “car,” “holiday”) handled naturally through reusable references—no manual profile ceremony.


What ANA Is Good For

  • Building an ANA‑Swarm: many small, specialized agents aligned by shared Mindset, policies, and output contracts.
  • Structured tasks at enterprise scale with up to ~92% token reduction while maintaining quality.
  • Faster responses, fewer re‑prompts, less knowledge leakage, and more natural chat flow.
  • In many workflows, an ANA‑Swarm can match or beat a much larger standalone model at a fraction of cost.


Commercial Outcomes

  • 🪢 Series Consistency: Cohesive multi-image/text campaigns across edits and versions.
  • 🖼️ Ult​ra-Large Image Tiling: Shard → generate → compose massive canvases without style drift.
  • đź’¸ 10×–100Ă— Cost Reduction: IDs, capsules, and reuse instead of replaying chat histories.
  • đź“‘ Regulatory Reports: Schema-locked outputs with citations and audit trails.
  • 🙋🏻‍♀️ Contact Center QA: Swarm grading + coaching at low token budgets.
  • 🏦 Deep-Tier Finance: Policy-aware scoring memos with shared Mindset and evidence.