Authored By
Premal Desai
Premal Desai is a Senior Vice President overseeing the product team in India for Arcesium. Prior to his current role, Premal was co-head of Arcesium’s Financial Operations group in India.
Share This post
Operations teams have taken robotic process automation (RPA), scripts, and machine learning (ML) rules engines about as far as they can over the past decade. These tools clear many routine breaks. But they struggle with the gray areas. When exceptions require human judgment for diagnosis, analysts can spend hours chasing anomalies across systems, inboxes, and counterparty chains. The shift to T+1 settlement in North America only added to the pressure.
Agentic AI promises a more flexible model of automation, however, by using reasoning and context instead of static logic. It is also designed to go beyond flagging by proposing resolutions and acting, as well as identifying when a potential resolution requires human review.
Agentic AI already enables many reviews to occur within the workflow, keeping a complete record of every action, exchange, and decision on-platform. We’re already starting to see it come into play today on our managed services teams for clients. As agentic AI evolves, more reviews can occur within the workflow, rather than through scattered emails, making oversight quicker, cleaner, and easier to audit.
Humans guiding strategy while machines multiply their reach is the essence of what we call the Hercules Effect.
Hercules, the great hero of Greek myths (and more recently, the Disney catalog), was capable of undertaking superhuman labors. He drew divine power into heroic human actions and accomplished labors no ordinary person could. His strength fused the wisdom and tools of the gods with human heroism and resolve.
In investment operations, the Hercules Effect goes far beyond typical “human in the loop” thinking. With traditional automation, people act at the end of the process, validating outcomes once machines are finished. Agentic AI turns that structure inside out. It multiplies human strength across every operational thread, giving them unprecedented reach and tools.
Agentic AI also learns in both directions. Every human interaction refines the system. As analysts resolve novel exceptions, feedback raises confidence thresholds and sharpens how agents match data and engage with counterparties.
Over time, the loop compounds: Teams spend less time on manual validation and more on designing escalation logic, monitoring drift, and reviewing transparent audit packs. Each cycle becomes faster, more consistent, and more governed, combining the speed of automation with the insight of experience.
Synchronizing human and machine intelligence works best through a structured, layered model. Well-tested ML rules and emerging agentic AI can operate in two tiers to combine stability with adaptability, each playing to its strengths while maintaining control in the hands of strengthened humans.
Tier one preserves the foundation. Deterministic rules manage the high-volume, repeatable mismatches that appear in daily operations. These rules follow defined logic, making their outcomes transparent and easy to audit. They create a consistent baseline that supports control, compliance, and speed.
Tier two adds intelligence. Agentic AI tackles the complex, exception-driven tasks that traditional rules cannot resolve. It performs fuzzy matching, interprets context, and triggers workflows that move cases toward resolution. Explicit thresholds and human feedback govern its behavior. Confidence defines when it acts and when it asks for review.
Rules anchor consistency, agents drive adaptability, and humans remain in command. Together, they form a synchronized system of control and intelligence.
Investment ops often bear the brunt of fragmented data and manual intervention. Reconciliation, settlement, and data management typically span multiple systems and inboxes, embodying the very definition of a complex, Herculean task.
Operational weaknesses like fragmented systems, slow communication, and manual resolution tend to show up in many workflows. Agentic AI addresses these challenges by maintaining a single, traceable workflow for every action and communication. Across reconciliation, settlement, and data management, it turns operational friction into structured intelligence.
For example:
Implementing agentic AI is as much about how you start as where you apply it. Success depends on structure, clean data, clear scope, strong guardrails, and transparent learning loops. Automating a messy process results in messy outcomes. The steps below offer a practical sequence for building confidence and scale.
Firms shouldn’t invest in agentic AI just because it’s trendy. The impact can be far-reaching. We see it as an integral element that marks a turning point in digital transformation. Fusing rule-based stability with adaptive intelligence creates operations that learn and strengthen with every cycle.
For institutions that depend on precision, this evolution means more than incremental improvements on operational KPIs. First, it mitigates compliance and investor relations risks. Second, it means building a decision infrastructure that grows smarter over time.
Transformation takes the Hercules Effect one step further. His story goes beyond superhuman strength. In mythology, he transforms from hero to immortal. Agentic AI represents a similar shift in operations. Manual effort gives way to a system that learns, scales, and sustains human judgment over time. The outcome is durable intelligence: workflows that improve themselves and organizations that grow stronger with every cycle.
No spam. Just the latest releases and tips, interesting articles, and exclusive interviews in your inbox every week.
No spam. Just the latest releases and tips, interesting articles, and exclusive interviews in your inbox every week.
