Emergent Behavior Can Be a Benefit, Not a Bug, for Leaders Adopting Agentic AI
CSAA Insurance Group’s Senior VP of Data Science explains how to harness favorable global outcomes from local actions
By Eoin Connolly
Agentic AI – systems of semi-autonomous agents that act on their own behalf – promises to reshape finance. Imagine networks of trading bots collaborating or competing, autonomous agents screening for fraud, or swarms of digital advisors optimising portfolios in real time. Yet beneath the hype lies a deeper, less discussed challenge: emergent behavior.
In a recent keynote talk for Corinium, Dr. Pipin Chadha, Senior Vice President of Data Science at CSAA Insurance Group, warned that emergent behavior can make or break agentic architectures. Understanding it, and designing for it accordingly, may be the single most important step financial institutions can take as they move toward autonomous systems.
“Local actions can produce unexpected global outcomes, sometimes beneficial, sometimes catastrophic,” Chadha says. “That’s the essence of emergent behavior.”
Emergent behavior occurs when individual agents following simple rules produce complex, collective patterns not obvious from the individual level. Nature is full of examples. Ant colonies coordinate without a leader. Bird flocks wheel around in unison without central control. Even our own neurons fire of their own accord in order to produce consciousness.
Financial systems themselves are complex, decentralized networks. Think markets, payment systems, credit networks. That’s why agentic AI carries an element of risk, despite being a conceptual fit. In markets, small changes in trader behavior can cascade into flash crashes or liquidity vacuums. The same risk applies to autonomous agents.
Well-intentioned but immature agents can cause more harm than good. In finance, an untested agent strategy could, in theory, misclassify legitimate transactions as fraud, or expose institutions to regulatory penalties. When dozens – or even hundreds – of such agents interact, predicting outcomes becomes exponentially harder.
Traditional AI applications in finance include scoring a loan, flagging a transaction, or classifying a document. Agentic AI changes the paradigm, and its autonomy makes emergent behavior inevitable. While it can unlock new efficiencies, like parallel fraud monitoring, it also introduces hard-to-debug systemic risks.
Errors can multiply unpredictably across agents, snowballing into an exponentially more challenging debugging landscape. And without shared context or ontologies, agents misinterpret each other’s signals. There can also be unforeseen collective effects. Local optimizations could, through a ripple effect, create global instability.
Using agent-based modeling to predict emergence
To deal with these challenges, Chadha recommends simulating emergent behavior before deployment. Agent-based modeling (ABM) and multi-agent simulation let designers test rule sets and see how agents interact at scale.
By tweaking parameters, developers can observe whether a network of agents converges to stable, desirable outcomes or spirals into chaotic oscillations. This approach mirrors stress testing in finance, but for behavior rather than balance sheets.
Key factors to evaluate include:
Adaptability: Do conditions change rapidly?
Concurrency: Do tasks benefit from parallel execution?
Collaboration: Do multiple entities need to coordinate without a central controller?
Risk tolerance: Can you afford emergent surprises?
This pre-defined clarity prevents “agent-washing,” where every component gets rebranded as an agent without real autonomy.
“Calling every function an ‘agent’ doesn’t make it agentic,” Chadha warns.
Communication and world models: the next frontier
A major barrier to robust agentic systems is communication. Humans rely on shared context and implicit world models; agents, at least in their current iteration, do not. Without standardized ontologies or protocols, miscommunication is inevitable.
Chadha points to new efforts like MCP – multi-agent communication protocols – as steps in the right direction, but stressed they don’t solve the deeper problem: agents still lack robust world models and counterfactual reasoning, the very capabilities evolution produced for humans over hundreds of millions of years.
Handled responsibly, though, emergent behavior can be a feature, not a bug. Swarms of agents could provide continuous, parallel fraud detection across networks, run market simulations to predict liquidity gaps or stress-test portfolios, and even offer personalised financial planning through collaborative digital advisors.
But these benefits will only materialize if institutions invest in rigorous design and oversight. Otherwise, small errors will cascade, producing flash crashes, false positives, or regulatory violations.
If, however, financial firms can master emergent behavior, they could create a new class of resilient, adaptive systems. This could mean markets that self-stabilize rather than self-amplify shocks, or customers served by personalized, collaborative digital advisors. But that outcome depends on humility as much as development. Agentic AI is not a shortcut to human-level intelligence, but rather a tool whose collective behavior must be designed, simulated, and monitored.
Agentic AI will shape the next generation of financial systems, but its defining feature – emergent behavior – is both its greatest strength and its biggest risk. By leveraging agent-based modeling and embedding robust guardrails, financial institutions can harness emergence for stability and innovation rather than chaos.
