Tech
Competitive
Aug 24, 2026
4 minutes
A private credit firm should evaluate a loan management platform by testing whether it can represent the firm's actual deal structures, govern material lifecycle changes, support AI agents as controlled operating actors, and connect safely with the firm's wider technology environment. A feature checklist and a standard product tour are insufficient because the differences emerge when a deal changes, an exception appears, or an agent attempts to act on the loan book.
The evaluation should therefore use representative workflows, source documents, integrations, and approval scenarios that reflect the way the firm expects to operate after implementation.
Before meeting vendors, map the work performed from approved deal through maturity and identify where the authoritative record should sit at each stage. Include onboarding, calculations, payments, notices, covenants, amendments, lender allocations, reconciliations, reporting, approvals, exception handling, and any workflows that an AI agent may prepare or execute.
The requirements should distinguish the work that must remain deterministic, the work that benefits from language interpretation, the decisions that require human authority, and the systems or agents that need controlled access to the record.
A useful requirements document separates three categories:
Core requirements: capabilities without which the firm cannot operate its current portfolio
Complexity requirements: structures, events, or controls that distinguish the portfolio from a standardized lending book
AI and connectivity requirements: agent workflows, external tool access, MCP or API connectivity, approval boundaries, and audit evidence
Future requirements: new strategies, funding models, service arrangements, integrations, or operating responsibilities that the platform may need to support
A scripted product tour usually follows the easiest path through the system, while a useful evaluation uses one or more anonymized facilities that reflect the firm's actual complexity and asks the vendor to configure and operate them.
The test should include the elements that create work for the operations team, such as multiple tranches, floating rates, PIK, custom fees, delayed draws, lender allocations, bespoke amortization, pricing grids, covenant definitions, unusual payment waterfalls, and effective-dated amendments. The objective is to see whether the platform models the structure directly or depends on external calculations and workarounds.
The quality of a loan management platform becomes clearer when an existing loan changes. Ask the vendor to process an amendment, rate reset, partial repayment, lender transfer, or pricing-grid step-down and show the active terms, calculations, lender positions, notices, workflow state, reports, approvals, and historical record affected by the event.
A field edit does not demonstrate lifecycle control. The vendor should show the effective date, source document, prior value, proposed update, reviewer decision, committed change, downstream recalculations, and the ability to reconstruct the previous state.
An AI demonstration should begin with a real operating input, such as a draw request, amendment, compliance certificate, payment notice, or reconciliation difference, rather than a generic question about the portfolio. Ask the agent to identify the event, retrieve the relevant terms and documents, call the required calculations, prepare the action, and route it through the platform's controls.
The evaluation should establish what the agent can read, which tools it can call, what it may prepare, what it may commit, and which actions always require human approval. It should also show how the agent handles uncertainty, conflicting evidence, missing information, duplicate instructions, and a failed downstream step.
A useful agent audit trail should preserve the triggering request, retrieved context, tool calls, calculation outputs, proposed changes, evidence, reviewer actions, and committed results. A transcript alone does not provide sufficient operational evidence.
MCP connectivity can allow agents in the firm's existing AI environment to interact with the loan management platform through domain-specific tools. The evaluation should focus on the scope and control of those tools rather than the presence of an MCP endpoint alone.
Ask the vendor to demonstrate how an external agent authenticates, how permissions are applied, whether read and write tools are separated, which loan records and documents are accessible, where approval is required, how every action is logged, and what happens when the agent requests an unsupported or unauthorized operation.
The strongest design exposes well-scoped loan operations, such as retrieving a schedule, reviewing notice history, preparing a rate reset, or opening an approval workflow, instead of providing raw database access. MCP is the interface through which the agent connects, while the platform's data model, permissions, workflow controls, and audit trail determine whether that connection is safe and useful.
Private credit operations require judgment, but judgment should remain visible and attributable. Review role-based permissions, maker-checker workflows, approval routing, segregation of duties, change history, exception handling, and the exact point at which a user, integration, or agent changes the authoritative record.
Ask the vendor to show a material action from preparation through commitment, including the original value, proposed value, supporting evidence, reviewer, approval time, resulting update, and every downstream effect.
A reporting demonstration should not stop at a dashboard. Select a figure and ask the vendor to trace it to the underlying facility terms, transactions, lender positions, and calculations, then change a relevant input and show how the report updates.
This test reveals whether reporting is produced from the operating record or from a separate dataset that must be reconciled, and it shows whether an AI-generated answer can return evidence rather than an unsupported summary.
The platform should connect structured loan data with the documents that govern it. Ask how executed agreements, amendments, compliance certificates, notices, borrower submissions, and other files are stored, versioned, searched, cited, and attached to operational actions.
For integrations, request a clear view of APIs, MCP tools, data ownership, export options, event handling, authentication, monitoring, versioning, and the process for adding or changing a connection. The loan book should exchange information with other systems without losing its role as the authoritative operational record.
Implementation quality depends on the condition of the source data, the complexity of the portfolio, the clarity of requirements, and the amount of configuration and integration involved. The workplan should identify responsibilities for extraction, cleansing, mapping, term validation, opening-balance reconciliation, workflow configuration, agent testing, user acceptance, training, and cutover.
Testing should include representative loans, lifecycle changes, permissions, external connections, and exceptions. A successful implementation is not established by loading data alone; the firm should confirm that the platform can reproduce agreed calculations, support the required controls, and complete the workflows that matter after closing.
Score each platform against the same demonstrated categories:
Deal and term flexibility
Post-close workflow coverage
Amendment and exception handling
Lender and funding-source management
Controls, approvals, and auditability
AI agent workflow execution and failure handling
MCP, API, and integration governance
Document grounding and evidence
Reporting traceability
Implementation and validation approach
Require a demonstration or documented answer for each score so presentation quality does not carry more weight than operating fit.
What is the most revealing demonstration scenario?
A lifecycle change or exception is usually more revealing than initial setup because it shows how the platform interprets evidence, updates connected records, applies controls, and preserves history.
What should firms test in an AI agent proof of concept?
The test should use a defined workflow with source documents, expected calculations, permission limits, approval steps, exception cases, and acceptance criteria for both the operational result and the audit evidence.
Does MCP connectivity make a platform AI-ready?
MCP connectivity provides an interface for agents, but AI readiness also requires structured loan data, domain-specific tools, permissions, approval controls, monitoring, and a traceable system of record.
Competitive
Tech
Aug 20, 2026
