RiskWise for the Era of Hyper-Automation: Proactive Risk Intelligence at Scale

Originally published on RiskWise.ai Insights.

“At the last dim horizon, we search among ghostly errors of observations for landmarks that are scarcely more substantial. The search will continue.”

— Edwin Hubble, on observing faint, distant galaxies

Risk in the era of hyper-automation

In February 2025, Anthropic published its Economic Index, constructed from millions of anonymized Claude conversations, which found that 36% of occupations now use AI to perform at least 25% of their tasks. Of these AI-assisted tasks, 43% are fully automated and 57% are used to augment human work. At the same time, large language model (LLM) agents are demonstrating increasingly scalable intelligence, unlocking automation in previously manual workflows and raising efficiency even where a human-in-the-loop remains necessary. Gartner projects that by 2027, 50% of business decisions will be augmented or automated by AI agents, a significant shift toward hyper-automation.

This era will fundamentally transform how organizations surface, analyze, research, and predict risk. Consider the insurance industry: according to Boston Consulting Group, AI can boost efficiency in complex underwriting by up to 36% and improve loss ratios by 3 percentage points by grounding analytics in new and previously inaccessible data. This highlights the business and competitive advantage of deploying advanced AI systems.

This is why we built RiskWise. RiskWise is a next-generation risk intelligence platform designed to scale and automate risk analytics and research. It continually ingests heterogeneous data, from news and social media to legal filings and academic research. RiskWise’s AI systems surface latent risk signals using proprietary LLMs, high-recall and high-precision search pipelines, and agentic workflows. On top of these systems sits a set of proprietary time-series and econometrics algorithms that construct risk indices encoding perceived and factual risk. Risk indices can compare any company, product, industry, or risk to improve data-driven decision making.

Figure 1. The RiskWise platform. Heterogeneous data is ingested, grounded by a hybrid retrieval pipeline, and processed by proprietary risk LLMs and discovery agents into risk indices tied to real outcomes. Every layer is exposed to external agents through MCP and A2A.

Hybrid retrieval augmented risk analytics

When AI interacts with data, its accuracy is bounded by the quality of that underlying data. RiskWise addresses this with a proprietary multi-stage AI search pipeline designed for a high signal-to-noise ratio. First, we execute a high-recall search conditioned on a specific risk to retrieve a candidate set of relevant documents from heterogeneous sources. Historically, inefficient human-in-the-loop workflows were required to preview results, so that search breadth did not compromise downstream results and initial queries were not overly broad, which drives significant downstream reranking costs. This is why we built our Query Builder Agent, which systematically evaluates queries across data sources and validates coverage before any costly reranking. We then deploy a series of efficient proprietary reranking LLMs that return only relevant documents. Our hybrid retrieval pipeline ensures that all subsequent risk analytics and research are grounded in verified, high-quality information.

Risk-driver discovery

RiskWise’s Driver Discovery Agent finds and extracts risk drivers, the factors that create or amplify risk, giving organizations clarity about why a particular issue poses a threat. For instance, ultra-processed foods (UPFs) present significant concerns to both insurers and consumer packaged goods companies, but what specifically makes UPFs risky?

It is the underlying drivers: links to cancer, increased cardiovascular disease, diabetes, and mental health disorders. These drivers lead to tangible negative events such as financial liability, regulatory scrutiny, and harm to reputation. RiskWise systematically monitors heterogeneous data channels, from scientific research and regulatory filings to news and social media, to surface and quantify these risk drivers. This lets our clients understand, predict, and proactively mitigate emerging threats before they become costly.

Flexible risk definitions

Our clients work across diverse industries, from consumer packaged goods (CPG) to insurance, and each measures risk differently. Insurers typically minimize total claim payouts; CPGs concentrate on detecting emerging risks and preventing product recalls and competitive incursions; and consumer tech brands are concerned about brand and product reputation. RiskWise is designed to model varying definitions of risk, whether litigation, regulatory action, financial loss, or other negative business outcomes.

Risk indices

Each risk, risk driver, company, and product is quantified using indices that capture emerging risk. These scores reflect changing perceived and actual risk, giving executives a real-time understanding of exposure. The indices are calibrated so that a higher score indicates more risk, and they remain comparable across topics. Indices can be grouped, filtered, and used as inputs to other machine-learning models.

Risk indices can be tied to outcome variables such as litigation, product recalls, and other events to directly measure a client’s definition of risk, not just the perceived and factual risk captured by our risk drivers.

Designed for the era of agents

RiskWise is engineered for an autonomous, agent-driven future, built on two open standards that ensure seamless, secure, and future-proof multi-agent workflows:

Agent access. RiskWise publishes its risk signals, drivers, and analytics through Model Context Protocol (MCP) servers. This standardized interface lets MCP-compatible agents securely access and interact with our data in real time to solve their own risk tasks.
Agent coordination. Once an agent accesses data via MCP, it can collaborate with other agents using the Agent-to-Agent (A2A) protocol. Agents discover each other, use each other as tools, delegate tasks, and orchestrate workflows autonomously and securely.

We anticipate RiskWise itself will become an industry-standard agent tool used across autonomous and augmentative risk workflows. This makes RiskWise both highly interoperable and future-ready.

Risk agents

RiskWise includes a suite of proprietary, specialized agents built on our internally curated risk research dataset and evaluation framework. Based on the Deep Research Bench evaluation pipeline, including the RACE and FACT metrics, our risk agents achieve state-of-the-art performance. Our static and dynamic risk agents and workflows are:

Query Agent. Tests search queries across heterogeneous data sources to ensure downstream risk analytics are grounded on the most accurate data. The first workflow in risk search.
Risk Driver Extraction Agent. Extracts the underlying drivers of perceived risk that could lead to negative events, through a series of iterative refinement LLMs and reasoning steps.
Risk Driver Deep Dive Agent. Conducts deep research into new or evolving risks, producing structured, cited, industry-standard, and actionable reports.
Narrative Development, Timeline, and Emerging Trends Agent. Constructs detailed chronological narratives linking risk drivers to observable events, letting organizations reconstruct causal chains.
Risk & Mitigations Deep Summarizer. Provides executive summary views of risk exposure and mitigation progress via dashboards, integrating evolving risk indices with recommended actions.
Risk Deep Research Agent. Lets users explore emerging risks through an interactive research assistant that ingests natural-language queries and returns responses with citations to external integrations and RiskWise. The superposition of our agent stack in a chat interface through tool use.

The emergent structure of risk

Traditional risk assessment looks at threats in isolation, so a product recall does not interact with future litigation. But what we are finding with our pipeline is that risk has an underlying structure that is emergent.

By modeling everything from PFAS contamination to generative-AI misuse, from ultra-processed foods to harm to brand reputation, we uncover patterns that are invisible when looking at any single risk type. This emergent structure of risk reveals how risks interact, amplify each other, and move across data sources, risk drivers, and industries to eventually lead to negative outcomes.

Figure 2. The emergent structure of risk. A faint early signal in one data source propagates across drivers and years into a materialized outcome. Each row is one of the example chains below; the shaded node is where the risk finally becomes costly.

Why does this matter? Some example chains:

Social-media concern about endocrine disruptors drives increased funding in peer-reviewed research, which then triggers litigation years later.
Brand-sentiment shifts on design forums precede the mass migration from Adobe to Canva, first visible in Google Search Trends before manifesting in quarterly earnings.
The release of GPT-2 generates academic papers on AI training datasets, which become cited evidence in copyright litigation against OpenAI years later.

These causal chains and interactions were always there. We can now surface and understand them with RiskWise AI.