This second blog in the three-part series examines how agentic AI is transforming commodity operations and why Operations Trade Risk Management (OTRM) represents the next phase in operational automation for commodity and energy trading firms.
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Computing has always been tied to work that is too slow, repetitive or complex for people to carry out by hand. From wartime codebreaking machines such as the Bombe and Colossus to ENIAC’s large-scale calculations and the Manchester Baby – which set the blueprint for modern computers – each generation has expanded the frontiers of automation.
Progress moved fastest where information could be organized, tasks were consistent, and rules could be applied repeatedly. In commodity and energy trading, automation began in the front office with the management of trades, positions, valuation, exposure, market risk and reporting. This evolved into the Energy and Commodity Trading and Risk Management (E/CTRM) systems that are now central to our industry.
E/CTRM brought improvements in structure, control and visibility that led to gains in productivity and efficiency. Firms sought to extend these benefits to their back office, but the data environment was far less accommodating:
These factors meant operations had little option but to continue as before. Institutional knowledge and human labor remained the primary defenses against risk. Leaders knew that manual processes and disjointed workflows meant missed signals, but that was the cost of doing business. The gaps between systems, teams, and operational workflows are where commodities risk management breaks down. Risk builds in the accumulation of unresolved exceptions across the trade lifecycle.
For years, it was either impossible or uneconomic for software to emulate the combination of operational knowledge, judgment and coordination. Commodity operations became a bastion of manual labor in a world of increasing automation.
The falling cost and rising accuracy of document digitization technology was the first breach. Machine learning made it affordable to structure information at scale and automate discrete tasks: accelerating standard processes, identifying patterns in large datasets, or calculating the risk exposure of specific events.
As artificial intelligence (AI) grew more sophisticated, and industry-specific applications were developed, automation began to propagate within operational workflows. Among the benefits, the most valuable was the ability to surface risks previously buried by data volumes and complexity. This was significant progress, but there was a problem. The disconnected datasets and fragmented systems that are common in commodity operations constrained automation to specific tasks.
The hardest part in commodity operations has always been the connective tissue. Risk can move from one function to another before anyone has a full view of the operational, compliance and financial impact. The underlying data exists in silos, but the real life of a trade doesn’t respect these boundaries. What firms needed was operational intelligence across the full lifecycle to see how events in one workflow affect exposure, position, and risk in another.
AI systems can process data, run tasks and identify problems. What changed with agentic AI was the ability to carry information and its context from one workflow to another. Agents can act on the insights the system generates: initiating the next step, routing the issue, updating a workflow or escalating the exception. This allows automations built for separate processes to work together, bringing the entire trade lifecycle into scope.
ClearOpx had the technology and experience to realize the potential of agentic automation. Our platform was built for the documents, data structures, terminology and workflows that define commodity and energy operations. The applications running on it – Trade Capture, Trade Confirmation, Operations Intelligence, Payment Processing and Finance Optimization – had already brought AI-enabled automation into the back offices of major trading firms. This gave our development team the benefit of direct insight into customers’ organizational and commercial goals, operating models and compliance requirements.
It was clear that the industry’s goals and frustrations pointed to the same need: operational automation that could work across workflows, systems and teams. Agentic AI made that ambition practical and reoriented our focus. Conversations with industry analysts helped define the shift as Operations Trade Risk Management, or OTRM™ – an automated operational layer that complements E/CTRM systems.
The ClearOpx OTRM Platform uses a roster of specialized agents. Application agents operate within our Intelligent Applications, executing core tasks such as trade capture, confirmation, reconciliation and settlement. Process agents connect and automate workflows across the trade lifecycle, managing validation, monitoring, routing and progression. Remote agents link with external systems and data sources, keeping workflows accurate, complete and up-to-date.
Together, these agents ingest, normalize, validate and reconcile data; monitor events that affect trade status and financial exposure; identify discrepancies and risk patterns; and trigger next-step processes. Low-risk tasks can be completed by agents, while exceptions are surfaced in real time with suggested remediation paths. Operations teams retain control, directing agents to execute the chosen corrective actions.
The practical effect is that risks are caught while they are still live. Confirmations can be checked against trade records and counterparty language. Invoices can be compared with actualized movements and supporting documents. Payment workflows can be flagged when documents are missing. Operational events can be assessed for their effects on settlement, pricing or contractual obligations.
When an exception needs human attention, it is routed to the relevant team along with the information needed to inform their decision. The process remains governed, evidenced and auditable, even as it encounters variations in documents, fragmented data and changing events.
Industry analysts have recognized OTRM as an emerging software category. E/CTRM and ERP systems remain essential, but OTRM addresses a different problem: the operational execution layer that determines whether a trade is carried out cleanly from capture through settlement.
The key difference is timing. The traditional operations model allowed too many issues to be missed or discovered downstream, after they had affected settlement, payment, compliance or financial exposure. The OTRM model allows issues to be identified while there is still time to act.
Data, events and exceptions move across the lifecycle with context attached. Movement updates can be assessed against exposure and cash flow. Letter of credit issues can be surfaced before they become financing problems. Payment and confirmation discrepancies can be resolved before they contaminate downstream workflows.
For operations teams, less time is spent chasing documents, reconciling mismatches and reconstructing problems after the fact. For senior leaders, execution becomes more measurable, risk can be identified and controlled earlier, revenue outcomes can be optimized, and growth is less dependent on adding headcount.
Operational automation of the full trade lifecycle will have far-reaching effects, but its basis in agentic AI demands enterprise-class controls. Commodity and energy trading operations handle sensitive data, including contracts, bank records and counterparty information. OTRM sits inside active workflows with the potential to affect trade economics, payments and contract terms. This has implications for financial, compliance and legal exposure.
Agentic systems must therefore be built around security, governance, traceability, auditability, validation and human oversight. Users and auditors need to associate dashboard data with source documents, understand why exceptions have been raised, and maintain command over approvals.
The balance between automation and control is part of the innovation. The latitude and behavior of automated systems must be governed and the outcomes evidenced. Human judgment remains in the process, but the work required to reach that judgment should be faster, better informed and easier to audit. OTRM can be introduced in phases, but it is prudent to understand the transformation that is underway.
Agentic AI has removed the major obstacles to operational automation. OTRM provides a structure that is practical, scalable and purpose-built for the commodity and energy trading industry.
In bringing a comprehensive solution to market, ClearOpx challenges operations leaders to rethink how commodity operations should be structured, scaled and measured. For the C-suite, it reframes operations from a cost center to a strategic lever for improved margin and growth. OTRM is where operational automation, commodities risk management, and agentic AI converge, giving operations teams the structure, visibility, and control to manage the full trade lifecycle.
Commodity Technology Advisory’s white paper, OTRM: The Emergence of a New Software Category in Commodity and Energy Trading Operations, explains the significance of OTRM and why it is set to define the next era of commodity trading technology.