If you’ve used ChatGPT, Midjourney, or a research assistant powered by generative AI, you’ve already met the single-agent model: one smart system that takes an input, processes it, and delivers a result. It’s useful, fast — and limited.

If you’ve used ChatGPT, Midjourney, or a research assistant powered by generative AI, you’ve already met the single-agent model: one smart system that takes an input, processes it, and delivers a result. It’s useful, fast — and limited. Even the most advanced reasoning setups we see today are usually just orchestrations of single agents, where one model handles planning, another expands prompts into detail, and yet another critiques the outcome. Helpful, yes — but still a linear chain, not a true system of agents learning and adapting together.

But what happens when you connect multiple AI agents, each with their own role, responsibility, and decision logic? What happens when they start talking to each other — not just executing tasks, but evaluating options, weighing alternatives, and coordinating strategies?

Welcome to the world of agentic AI. It’s not a theory. It’s not AGI. It’s a modular way of building systems — and it’s already reshaping how knowledge work, and increasingly financial decision-making, gets done.

This is the first chapter in our new Behind the Cloud series, where we explore how the Omphalos platform is evolving from rule-based signal generation to something far more dynamic: an ecosystem of intelligent, aware, and eventually collaborative trading agents.

What Is Agentic AI?

At its core, agentic AI refers to artificial intelligence systems designed as agents — autonomous units capable of perception, reasoning, and action in pursuit of a defined goal. Each agent may specialize in a task: answering a question, analyzing data, critiquing a plan, or generating code.

The power of this approach doesn’t lie in any one agent, but in how agents interact. When you connect them — often with memory, communication protocols, and feedback loops — you get a multi-agent system.
Unlike traditional AI pipelines, where logic flows in one direction, agentic systems allow for:

• • Dialogue (agents critique or refine each other’s output),
  • Division of labor (each agent focuses on what it does best),
  • Autonomy within constraints (agents can act independently within guardrails),
  • and increasingly, collaboration toward a shared goal.

These systems are already being deployed — often behind the scenes — in research labs, product development, and even finance.

These systems are already being deployed — often behind the scenes — in research labs, product development, and even finance. Their true strength lies in adaptability: because agents can critique, refine, and coordinate with each other, they evolve faster than single models. Instead of being locked into static pipelines, agentic systems adjust in real time — learning from feedback, shifting strategies, and improving outcomes as environments change.

Why This Matters for Finance

Financial systems are a natural fit for agentic AI — not because they’re simple, but because they’re modular by nature. In a typical investment process, different stages already follow a kind of agent-like logic:

• • Market scanning
  • Signal generation
  • Portfolio construction
  • Risk assessment
  • Execution and rebalancing
  • Review and refinement

Each of these steps can be decomposed into smaller decision-making tasks — making them well suited for agent-based architectures.

At Omphalos Fund, we didn’t adopt this because it was trendy. We built it in because it was practical:

• • Hundreds of specialized agents generate signals across timeframes, regions, and instruments.
  • These agents operate independently, but within a hardcoded system of constraints and rules.
  • Performance is auditable, repeatable, and scalable.

But more on that in Week 2.

What the Research Says

The shift from single-agent to multi-agent AI systems isn’t just theoretical — it’s backed by growing empirical evidence.

Here are some highlights from recent research:

1. Microsoft AutoGen (2023)
In a large-scale study by Microsoft Research, agent teams built with AutoGen — a framework enabling agents to communicate via natural language — outperformed single-agent setups by up to 45% on complex reasoning tasks. These included multi-step problem-solving and planning workflows, which directly relate to structured decision environments like investing.

Key takeaway: Agent collaboration increases both success rate and solution depth.

2. CAMEL: Stanford/Shanghai Jiao Tong (2023)
The CAMEL framework (Communicative Agents for Mind Exploration of LLMs) showed that when agents were assigned roles (e.g., “CEO,” “CFO”) and placed into role-play dialogues, they consistently produced better and more rational strategies than isolated agents. This approach supports role specialization and mirrors how trading systems could distribute logic among agents with distinct mandates.

Key takeaway: Role-playing agents improve reasoning consistency and reduce hallucinations.

3. LangGraph & LangChain Experiments (2024)
Open-source communities have begun using LangGraph and LangChain to build real-world multi-agent applications in finance, including compliance bots, trade assistants, and risk monitoring agents. Reports show faster iteration, lower error rates, and better traceability of agent decisions due to modular workflows and explicit memory states.

Key takeaway: Modular agent systems enhance auditability and reduce failure propagation.

Frameworks That Make It Work

These research findings are now being turned into practical tooling. Leading frameworks include:

• • AutoGen (Microsoft) – Ideal for building dynamic, dialogue-based agent teams
  • LangGraph / LangChain – Flexible orchestration frameworks with memory, logic, and observability
  • CAMEL – Role-based reasoning with specialized agent interactions

All of these help break up complex tasks and enable smarter systems without centralizing all logic in a monolithic model.

What This Is NOT

Let’s be clear: we are not talking about AGI — Artificial General Intelligence.

Our interest at Omphalos lies not in creating all-knowing machines, but in building better systems: systems that are

• • modular,
  • reliable,
  • and designed to perform under real-world constraints.

Agentic AI is not a replacement for expertise or governance — it’s a way to extend intelligence into systems that are too complex to centralize, and too dynamic to hardcode entirely.

Where This Is Going

In the coming two weeks, we’ll explore how this applies to trading systems in practice. We’ll show:

• • How Omphalos agents already produce signals under strict risk control logic (Chapter 2)
  • And how the next generation of trading agents could make strategic decisions, develop awareness of each other, and coordinate to improve total portfolio performance (Chapter 3)

Each stage builds on the same idea: breaking complexity into intelligent parts, then letting those parts think together.

We hope you’ll join us on this journey.

👉 Next stop Chapter 2: The Existing Trading Agents at Omphalos Fund – Signals Without Emotions