marvis-risk-agent

From a request to a reviewable result

MARVIS is a local-first, governed credit-risk Agent platform—not a chatbot wrapped around a collection of scripts.

Describe the business outcome in natural language. MARVIS asks for missing files and definitions, builds a reviewable plan, pauses at responsibility gates, runs deterministic tools, and returns real datasets, evidence, models, strategy code, and reports.

flowchart LR
    A["Describe the risk goal"] --> B["Agent clarifies inputs and definitions"]
    B --> C["Validated workflow plan"]
    C --> D{"Human confirmation<br/>where required"}
    D --> E["Deterministic tools execute"]
    E --> F["Evidence, artifacts, and reports"]
    F --> G["Review, adopt, and iterate"]

What you will notice in V3

• A task workspace you can follow. Progress, materials, confirmations, results, and next actions stay together. The sidebar groups tasks by what needs your attention, what is running, and what you worked on recently.
• English and Chinese, light and dark. Switch the interface language or theme from Settings. Your task names, filenames, field names, and original values stay as you entered them. Unsent reply-language hints and report edits survive interface refreshes.
• Clearer recovery when work stops. Diagnostic cards explain the problem and offer supported recovery choices, with action history, budgets, and undo where available. Dependency installation remains an explicit user action.
• Less setup between conversation and execution. Start SQL extraction or monitoring schedules from a conversation, review the populated form, and confirm. Changing a monitoring dataset refreshes available time and label fields while preserving valid selections.
• Smoother modeling handoffs. Sample maturity, score direction conflicts, tuning confirmation, model naming, and report wording make the next decision clearer across feature analysis, modeling, validation, and monitoring.
• Results that remain reviewable. Deterministic evidence and downloadable reports stay attached to the task; stale report confirmations are rejected without leaving a queued job behind.

Real application screenshots using synthetic demonstration data; no customer records or paid-model responses. See the V3.0.0 release notes for the full changes and acceptance boundaries.

Why risk teams use MARVIS

Risk teams need to turn a change in risk into a considered response, retain the reasoning behind it, and repeat the work without rebuilding the same analysis. MARVIS brings those needs into one governed workspace. The aim is to put AI at the center of workflow coordination, with people supervising risk boundaries and consequential decisions, while each round of work adds traceable, reusable knowledge to the organization.

Develop a targeted response when risk changes. When a customer segment deteriorates or new fraud-attack signals emerge, teams need to understand the affected populations, variables, and rules before trading off approval, risk, and return. MARVIS connects data investigation, feature analysis, candidate strategies, backtesting, and impact measurement in one task. The goal is to shorten analysis and iteration, giving teams evidence for selective adjustments instead of relying only on blanket tightening. The current workflow supports local analysis, validation, and human confirmation; live blocking still requires institution integration, deployment, and authorization.

Keep risk knowledge and experience within the organization. Metric definitions, field meanings, historical performance, and lessons learned should remain useful beyond an individual script or conversation. Tasks retain materials, parameters, results, deliverables, and decision records. Memory reuses bounded preferences, field definitions, and historical experience with source references, so a new task can revisit the basis for an earlier judgment. Teams still review whether that context applies: historical explanations cannot overwrite current calculations, and experience does not establish business performance on new data.

Reduce repetitive engineering so business users can cover more work. Start with a business objective. The Agent asks for missing materials and definitions, then organizes a reviewable plan. Existing workflows connect data preparation, model comparison, strategy assessment, and reporting, reducing repeated script assembly and manual transfers between steps. Business users can advance more analysis, modeling, and strategy work within supported capabilities and governance boundaries, while specialists retain responsibility for definitions, assumptions, consequential choices, and acceptance.

Workflows organize the steps; Tools expose callable capabilities. Plugins register tools and permissions, while Hooks connect follow-up actions to events such as step completion and report generation. A Skill is a user-authored Workflow template for reusing established practices. Agent-driven progress, manual confirmation, and result review share task materials and evidence. Key metrics are computed by platform code; consequential actions require confirmation, and materials and evidence stay in the local workspace by default.

Package domain experience as reusable capabilities. A risk workflow brings together task knowledge, trusted tools, input contracts, exception handling, checks, and deliverable templates. Institutions can adapt supported Workflow templates to their own definitions while retaining platform validation and permissions. Memory adds source-linked experience; plans and execution records make the work inspectable. This combination of credit-risk practice, deterministic calculation, governance, and a dedicated workbench is MARVIS's core value beyond general-purpose Agent orchestration.

One workbench for end-to-end local risk analysis and development