Peak3 has launched Graphene v4 and Graphene Harness, combining an AI-native insurance core with an AI-driven software delivery lifecycle designed to build, test, migrate and maintain insurance systems.
The next phase of AI adoption in insurance may be less about adding copilots to existing workflows and more about changing how the industry’s underlying software is built and operated.
That is the bet behind Peak3’s Graphene v4 and Graphene Harness, two products designed to put artificial intelligence deeper into the insurance technology stack. Graphene v4 provides an AI-native insurance core and governed agent platform, while Graphene Harness applies AI across the software delivery lifecycle itself.
The Singapore-based company says the combined approach is intended to address one of insurance technology’s persistent problems: modernizing complex, highly regulated core systems without relying entirely on lengthy, labor-intensive implementation cycles.
The timing is significant. McKinsey reported in 2026 that insurers are moving along an “AI staircase,” from established predictive analytics toward generative AI and emerging agentic AI capable of managing more complete workflows. The consultancy also identified modernization of insurance core technology as a potential use case for agentic AI, particularly where agents can coordinate migration, testing and other complex IT tasks.
Peak3 is attempting to take that concept into the software production process.
From AI applications to an AI production line
Graphene Harness is described by Peak3 as an end-to-end AI-driven delivery lifecycle, or AI-DLC. Instead of using AI for a single activity such as code generation or software testing, it connects multiple delivery functions into what the company calls a virtual scrum team.
The system incorporates digital representations of roles including business analysts, architects, engineers, testers and reviewers. These AI agents are connected to a curated knowledge base and AI-driven capabilities for testing, migration, diagnostics and CI/CD.
That distinction matters.
AI coding assistants can accelerate individual development tasks, but insurance modernization involves much more than writing software. Requirements need to be translated into system behavior, legacy data must be migrated, changes have to be tested against complicated business rules, and production systems must remain auditable.
Peak3 says it is already using Graphene Harness to build and maintain Graphene itself. The company is making the technology available initially to selected implementation partners and expects to extend access to clients over the next six to 12 months.
The company also says its forward-deployed engineering teams will work alongside customers on AI-first operations spanning both Peak3 platforms and existing technology stacks.
Graphene v4 puts agents closer to the core
Graphene v4 adds the Graphene Agent Platform, which Peak3 describes as model-agnostic infrastructure for building, running, observing and governing AI agents.
The platform includes pre-built agents targeting use cases such as medical underwriting, conversational first notice of loss, intelligent document processing and claims fraud detection.
More significantly, Peak3 says agents can interact directly with insurance product configurations, calculations and business rules. Unstructured documents and natural-language instructions can be used to modify configurations, with work that traditionally takes days potentially reduced to minutes or hours.
That moves AI beyond assisting employees and toward changing the configuration of the systems those employees use.
For an insurer, however, that introduces governance challenges. An agent capable of modifying pricing logic, policy rules or product configurations cannot be treated like a conventional chatbot.
Peak3’s architecture therefore emphasizes controlled access, attribution and detailed logging. Graphene exposes capabilities through APIs, the Model Context Protocol (MCP) and command-line interfaces, allowing agents to interact directly with the core instead of navigating a graphical user interface.
The company says those controls are enforced at the code level rather than depending solely on prompt-based guardrails.
Graphene can also operate as a standalone middle layer between AI agents and existing systems of record. That could provide insurers with a path toward agent-ready capabilities without immediately replacing their core platforms.
Why the architecture matters
Insurance remains one of the more difficult enterprise environments for AI because its workflows combine large amounts of structured and unstructured information with regulatory requirements and complex business rules.
McKinsey’s research has found that insurance AI leaders have generated substantially stronger shareholder returns than laggards, while also arguing that insurers need comprehensive technology and operating-model changes rather than isolated AI deployments.
The architecture Peak3 is proposing addresses that modernization challenge from two directions.
Graphene v4 creates a core environment designed for AI agents, while Graphene Harness attempts to automate more of the work required to build and change that environment.
The company is also broadening Graphene across insurance lines. The latest version supports group health workflows including provider network management, commercial lines with co-insurance and reinsurance, indexed universal life and crypto-denominated policies, as well as Takaful alongside conventional insurance operations.
That breadth positions the platform against established insurance core technology vendors while also reflecting a wider shift toward modular, AI-compatible systems.
McKinsey has argued that insurers are moving away from monolithic environments toward more modular architectures where specialized AI tools can interoperate with core systems.
The economics of AI-driven delivery
Peak3 says the commercial motivation is straightforward: reduce the relationship between software delivery cost and the number of people required to perform the work.
The company reports that deployments using Graphene Harness have already achieved a 50% reduction in end-to-end new-feature development and core-system implementation costs, with corresponding reductions in delivery timelines. Peak3 has set a longer-term target of 80% cost reduction within 18 to 36 months.
Those figures are company-reported rather than independently validated, and the eventual economics will depend on the complexity of individual insurance implementations.
Still, the underlying proposition is increasingly relevant. AI-assisted software engineering is moving toward multi-agent development systems that can coordinate requirements, coding, testing and deployment rather than simply autocomplete individual functions.
For insurers, that could make modernization less dependent on large implementation teams and lengthy delivery cycles.
The competitive question is whether these AI-driven delivery systems can maintain reliability and regulatory control while operating at the scale and complexity required by insurance.
Peak3 says its approach gives insurers greater ownership over how their core systems are changed, rather than making them dependent on a vendor’s delivery organization.
If that model works at scale, the more important innovation may not be the AI agent performing underwriting or claims work. It could be the AI production system that continuously builds, tests and adapts the technology underneath those agents.
Market Landscape
Insurance technology is shifting from legacy modernization and workflow automation toward agent-ready core architectures. Vendors are increasingly competing on whether their platforms can expose insurance capabilities to AI while maintaining governance, security and auditability.
McKinsey identifies agentic AI as a potential catalyst for insurance core modernization, particularly in testing, reconciliation, migration and cutover.
Peak3’s strategy combines two trends: AI-native insurance cores and AI-assisted software engineering. The competitive field includes established core-system providers, cloud platforms, AI engineering vendors and emerging insurance technology companies building modular architectures.
The critical differentiator will be whether AI can accelerate modernization without compromising the controls required for regulated insurance operations.
Top Insights
- Peak3 is applying AI not only to insurance workflows but to the software lifecycle used to build and maintain insurance systems.
- Graphene v4 provides a model-agnostic agent platform with governance, observability and direct API, MCP and CLI access to core capabilities.
- Graphene Harness connects AI agents across analysis, architecture, engineering, testing, migration, diagnostics and CI/CD.
- Peak3 reports a 50% reduction in new-feature development and core implementation costs, with an 80% target over 18–36 months.
- The approach reflects a broader insurance shift toward modular, agent-ready core technology and AI-assisted modernization.
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