Beyond LLMs: What it takes to build reliable infrastructure for agentic trading

By Ramakant Yadav, Founder, Scalar Field

Every few weeks, a new AI model tops a leaderboard, and the industry declares that AI agents have arrived. But when those agents are trusted with real capital, a less glamorous reality becomes clear: the model is only a small part of the system. In agentic trading, the language model may represent perhaps a tenth of the engineering challenge. The other 90 per cent lies in infrastructure — execution, risk controls, state management, reconciliation and auditability. These are not the technologies that dominate AI headlines, but they determine whether an autonomous system can actually be trusted with money.

Agentic trading represents a significant shift from conventional investment technology. A person can express a market belief in plain language, while an agent researches the idea, tests whether it has worked historically and, within an approved mandate, executes and manages positions on live markets. Once real money enters the equation, the standard changes completely. A chatbot that is usually right may be impressive. An order path that is usually right is a liability.

Keeping AI Away from the Critical Execution Path
The first principle of reliable agentic trading is to keep the language model away from the critical execution path. Many agent frameworks assume that every action begins with a model call: an event occurs, the model reasons about it and the system acts. That approach is poorly suited to financial markets, where execution needs to be fast, predictable and repeatable. Model calls can introduce latency, cost and non-deterministic behaviour — precisely the characteristics that become problematic when an order has to be placed, modified or cancelled within milliseconds.

A stronger architecture separates intelligence from execution. AI should operate where interpretation creates genuine value: reading regulatory filings, parsing news, identifying market signals, developing investment theses and revising those theses as new information emerges.

Once that reasoning has been translated into an approved strategy, execution should move into a deterministic, event-driven layer that can monitor markets and react without requiring another model decision at the point of trade. Risk checks, position limits, order validation and execution logic should remain conventional, testable software. The agent can think slowly; the trading system must act fast. Most importantly, an autonomous agent should never be allowed to improvise when real money is moving.

The Invisible Challenge: Knowing What Is Actually Owned
The second challenge is less visible but potentially more damaging: state accuracy. How does an autonomous system know what it actually owns? The question sounds simple until real-world trading begins. Orders can be partially filled. Brokerage APIs can fail midway through a request. Network interruptions can trigger retries. A human may simultaneously trade from the same account while an agent is operating in the background. Each of these situations can create a gap between what the agent believes and what the broker or exchange actually records.

That gap can have serious consequences. An agent working from a stale internal ledger could attempt to use shares that another strategy is already relying on, miscalculate available capital or make a risk decision based on positions that no longer exist. This is not primarily an AI problem. It is a financial systems problem. The answer is continuous reconciliation. The core principle is simple: the aggregate positions allocated to agents should never exceed the positions confirmed by the trading venue. Orders need to be idempotent, so a retry cannot unintentionally create a duplicate trade.

Agents need explicit claims over their allocated capital or positions, preventing one strategy from consuming another’s resources. Internal records need to be continuously compared with venue records rather than reconciled only at the end of the day. When an agent’s protected holdings fall below the reconciled threshold, the safest response is not to let it guess.

The system should freeze it. A frozen agent is an operational inconvenience; an autonomous system continuing to trade on an incorrect view of reality can turn a small systems error into a financial loss. Reconciliation, therefore, is not merely bookkeeping. It is the layer that makes autonomous trading auditable, controllable and financially credible.

Accountability Still Belongs to Humans
The third challenge is accountability. Greater autonomy does not mean that human responsibility disappears. The human still defines the mandate — the investment thesis, risk limits, capital allocation and boundaries within which an agent is permitted to operate. The agent takes care of the continuous work: watching markets, waiting for conditions, responding to events and executing approved actions. Responsibility, however, remains with the human.

That makes auditability a core infrastructure requirement. Every significant action should be recorded and capable of being reconstructed later. It should be possible to understand what the agent knew, what information it considered, what decision it made, which rules were applied and how that decision translated into an order. Without such a trail, post-trade review becomes guesswork. If a trade cannot be reconstructed after the fact, the system is not truly accountable. It is simply an automated process whose behaviour cannot be adequately explained.

These principles extend beyond financial markets. Any enterprise that allows AI agents to act on a system of record will eventually encounter the same fundamental problems. Payment systems need to ensure that an agent cannot execute the same transaction twice. Inventory platforms need to ensure that an agent’s view of stock matches actual availability. ERP systems need controls against conflicting actions by multiple autonomous processes.

Logistics systems need reliable state management when circumstances change faster than an agent can reason. Across these environments, three requirements consistently emerge: deterministic execution, continuous reconciliation and accountability through auditability. India is particularly well positioned to build this infrastructure because the country already operates large-scale digital financial and back-office systems where transaction integrity, reconciliation and reliability are fundamental requirements. Agentic AI introduces a new participant into these systems, but the underlying engineering discipline remains familiar.

Conclusion: Trust Will Be Built Below the Model
Within the next few years, autonomous agents are likely to become meaningful participants in financial markets. The important question will not simply be whether an AI agent can analyse a market, identify an opportunity or generate a trading strategy. The harder question will be whether the surrounding system can ensure that the agent acts within its mandate every time.

That trust will not come from AI leaderboards alone. It will come from boring but essential engineering: deterministic execution, accurate state, continuous reconciliation, strict risk boundaries and an audit trail that can withstand scrutiny. The biggest competitive advantage in agentic trading may therefore sit below the model. The winners will not necessarily be those with the most sophisticated AI, but those capable of building the infrastructure that allows AI to operate safely in the real world. The future of agentic trading will be defined not just by how intelligently machines can think, but by how reliably they can act.