CEO, Symphony Fintech
Artificial Intelligence is already becoming familiar to most of us. We ask AI to write an email, summarize a document, analyze information or answer a question.
But a new form of AI is emerging: Agentic AI.
The easiest way to understand the difference is this:
Traditional AI answers a question. Agentic AI can understand a goal, determine the steps required, use available tools and take actions toward achieving that goal.
This distinction could have significant implications for capital markets—and particularly for algorithmic trading.
Automation cannot manufacture an edge out of a tool everyone owns. When the same models are available to every participant at similar cost, the model stops being the advantage. What you do with it becomes the advantage.
And it cannot correct a decision that was poor to begin with. According to SEBI’s data, more than half of index options turnover in India now sits in contracts expiring the same day. Automating a same-day-expiry position does not make it a strategy. It makes it a faster one. Speed helps a good decision and multiplies a poor one, and the machine has no opinion about which it is executing.
From Algorithms to Agents
Algorithmic trading itself is not new. For years, trading systems have been able to execute orders automatically based on predefined rules.
An algorithm may be instructed: If the price reaches a certain level, execute an order, or slice this large order into smaller orders over the next two hours.
The algorithm is fast and disciplined, but it essentially follows logic defined in advance.
An AI agent introduces a different possibility. Instead of merely executing a predefined strategy, it could potentially understand an investor’s objective, analyze changing market conditions, evaluate alternatives and decide what steps should be considered next.
That moves us from rule-based automation towards goal-based or intent-driven automation.
A Simple Example
Imagine an investor with a portfolio of equities and derivatives.
Today, the investor may need several screens to monitor market prices, positions, profit and loss, news, risk and margin requirements.
An AI assistant could simplify this. The investor might ask:
“Why is my portfolio down today?”
AI could analyze the portfolio and explain which stocks, sectors or derivative positions are contributing most to the loss.
Agentic AI could potentially go further.
An AI agent could continuously monitor the portfolio. If risk suddenly increases, it could identify the source, calculate possible hedges, assess their impact, check available margin and present the investor with possible actions.
The investor could then approve the appropriate action before an order is sent.
The important difference is that AI has moved from explaining what happened to helping manage what should happen next.
What Could This Mean for Brokers?
The opportunity becomes even more interesting at the broker and financial-institution level.
A broker may handle thousands of clients and potentially millions of orders, positions and risk calculations.
An AI agent could continuously monitor this environment—identifying unusual trading activity, rapidly increasing client exposure, margin stress, abnormal order patterns or operational exceptions.
Consider a risk-management system. Today, predefined RMS rules may trigger alerts or reject orders when limits are breached.
In the future, an AI agent sitting alongside the OMS/RMS could potentially interpret multiple signals together, identify an emerging risk before a hard limit is breached, determine which accounts or positions are responsible, and bring the relevant information and possible actions directly to the risk manager.
Instead of people constantly searching through data for problems, AI could find the problems, investigate them and bring the important decisions to people.
From Copilot to Agent
This represents an important change in how we think about AI.
A Copilot helps a human perform a task.
An AI Agent can perform parts of the workflow on behalf of the human, within clearly defined boundaries.
A future trading workflow might therefore look like:
Investor Intent → AI Analysis → Strategy Alternatives → Risk & Compliance Check → Human Approval → Order Execution → Continuous Monitoring
An investor might simply say:
“I want to reduce my portfolio risk if the market falls sharply.”
The AI agent could analyze the portfolio, identify major exposures, evaluate possible hedging strategies and explain their costs and trade-offs.
The interaction therefore shifts from navigating multiple complex screens to expressing an investment intent in natural language.
But Should AI Be Allowed to Trade by Itself?
This may be the most important question.
Financial markets involve real money, and incorrect actions can create losses within seconds. AI models can also make mistakes or behave unpredictably when faced with situations outside their expected context.
Agentic AI therefore cannot simply be given unlimited freedom.
In capital markets, the practical model is likely to be bounded autonomy.
AI agents will need to operate within predefined permissions, position and risk limits, compliance rules and approval thresholds. Critical controls should remain deterministic rather than depending solely on an AI model.
Equally important will be auditability and accountability. Institutions must be able to establish what an AI agent observed, what it recommended, what action was authorized and why that action was ultimately executed.
The Bigger Picture
For many years, capital-market technology has concentrated on making trading faster—lower latency, faster market data and faster execution.
Agentic AI introduces a different dimension: making trading systems more intelligent and more intuitive.
The next generation of trading platforms may not simply display market data and provide buttons to buy or sell. They could increasingly understand what an investor is trying to achieve, analyze the situation, propose appropriate actions and help execute them safely within defined controls.
The evolution could therefore be more profound than simply adding AI features to today’s trading terminals.
We may gradually move from screen-driven trading to conversation-driven trading, and from instruction-driven algorithms to intent-driven agents.
