Multi-agents AI: what exactly is it? (Part 1)

 Multi-agents AI: what exactly is it? (Part 1)

But first, why do we need it
Most AIs today still fall into one of two categories:

1- Over-reliant on a single large model → prone to mistakes, loops, and unpredictable behavior.
2- Predefined workflows → more reliable but rigid and hard to scale.

Neither truly enables AI to handle real tasks independently.

Multi-agent AI takes a different approach. Instead of one AI doing everything, multiple specialized agents work together dynamically to complete tasks efficiently.

One might gather information, another analyzes it, and another takes action—they communicate, adjust plans, and track progress, just like a well-coordinated team.

Here’s how it happens/ tech breakdown:

1️⃣ Role Assignment & Task Delegation
At the core of any multi-agent system, there’s usually an Orchestrator Agent (or Coordinator).

This agent is responsible for: Breaking down the task; Deciding which agents are needed; Delegating work based on agent capabilities

2️⃣ Communication & Information Sharing
Agents exchange data through APIs, message passing, or shared memory.

This allows them to:
- Share insights in real time
- Adjust workflows dynamically based on new information

3️⃣ Reflection & Self-Correction
Unlike single-agent AI, multi-agent systems track progress and self-correct using:

- Task Ledgers (tracking what’s been done vs. what’s left)
- Feedback Loops (agents double-check their work)
- Dynamic Replanning (if an approach fails, agents adjust strategy)

4️⃣ Multi-LLM & Specialized AI Models
Instead of using one large LLM for everything, multi-agent AI systems combine:

- A generalist LLM for reasoning and orchestration
- Small fine-tuned models for specialized tasks

5️⃣ Execution & Continuous Learning
Once agents complete a task, multi-agent systems don’t just stop—they learn from each execution to improve performance.

And this isn’t theoretical, it’s already happening. A few examples:

🚗 Tesla’s Full Self-Driving (vision, path planning, and decision-making agents working together)
💰 Goldman Sachs AI Trading (market analysis, risk management, and execution agents)
🔬 Recursion AI in drug discovery (analyzing biological data, predicting drug interactions, and optimizing trials)

නමුත් පළමුව, අපට එය අවශ්‍ය වන්නේ ඇයි අද බොහෝ AI තවමත් කාණ්ඩ දෙකෙන් එකකට වැටේ:

1- තනි විශාල ආකෘතියක් මත අධික ලෙස රඳා පැවතීම - වැරදි, ලූප සහ අනපේක්ෂිත හැසිරීම් වලට ගොදුරු වේ. 2- පූර්ව නිශ්චිත වැඩ ප්‍රවාහ → වඩා විශ්වාසදායක නමුත් දෘඩ සහ පරිමාණය කිරීමට අපහසුය.

සැබෑ කාර්යයන් ස්වාධීනව හැසිරවීමට AI හට සැබවින්ම හැකියාව ලබා නොදේ.

බහු-නියෝජිත AI වෙනස් ප්‍රවේශයක් ගනී. එක් AI එකක් සියල්ල කරනවා වෙනුවට, බහු විශේෂිත නියෝජිතයින් එක්ව කාර්යයන් කාර්යක්ෂමව සම්පූර්ණ කිරීම සඳහා ගතිකව ක්‍රියා කරයි