Built on EVOQ, our AI-native operating layer, where human-defined context and architecture guide autonomous execution across engineering, data, platforms, and operations.
Agentic engineering is how Myridius turns services into systems
Work is decomposed differently, execution is orchestrated through agents, and human intervention shifts toward context, architecture, policy, and exception handling.
Locating handoff drag, wait states, rework, and routine engineering effort
Defining where humans lead, where agents execute, and how approvals are governed
Connecting context, tools, repositories, telemetry, and policies into one operating flow
Moving teams from experimentation to governed AI-native execution
Short-cycle execution replaces handoff-heavy project work
Myridius structures execution around business context, system boundaries, and governed cycles so agents can plan, build, validate, and release rapidly.
Context capturing the business outcome, constraints, architecture, and success criteria
Systems decomposing the change into coherent execution systems with clear interfaces and dependencies
Cycles running 1-3 day bursts where agents build, validate, document, and prepare release-ready output
Human checkpoints approving priorities, design choices, and policy exceptions at defined moments
A new operating rhythm for delivery at scale
Myridius establishes a connected system integrating context, execution, validation, release, and learning into a scalable framework for engineering, modernization, and operations.
Structuring greenfield, brownfield, and workflow transformation into governed execution blocks
Running legacy transformation, cloud transition, platform upgrades, and infrastructure renewal through the same operating rhythm
Coordinating pipelines, APIs, enterprise workflows, semantic context, and shared services with traceable memory
Feeding telemetry, incidents, usage signals, and support patterns back into planning, validation, and recovery
Evoq is the runtime for autonomous execution
Engineering systems run through Evoq, our AI-native operating layer that carries context, coordinates agents, enforces policy, and turns delivery knowledge into reusable execution components.
Preserving requirements, architecture, code, and operational memory across the lifecycle
Coordinating planner, builder, validator, launcher, and governor roles
Embedding human approvals, auditability, and release governance into execution
Converting solved work into repeatable delivery assets that compound over time
Value scales up as effort scales down
By shifting to agentic execution systems on Evoq, Myridius changes the economics of software engineering, increasing throughput, lowering cost per unit, and expanding reuse.
Through short-cycle, agent-led execution
Reduce routine engineering effort and eliminate handoff friction
With automated legacy translation and governed parity validation
As agents absorb repeatable support and upkeep work
Evoq connects context, agents, controls, integrations, and reusable components for scalable enterprise execution.
Enterprise orchestration for agentic execution
Where product delivery, modernization, cloud transition, platform change, data movement, and integration complexity drive revenue, compliance, resilience, and customer trust.
Implemented an Agentforce-powered agentic AI solution on Salesforce Data Cloud that continuously scored churn risk, surfaced root causes, and triggered targeted mitigation actions across a multi-product SaaS ecosystem.
A leading fleet operations platform had churn signals scattered across usage data, service interactions, and onboarding history, leaving teams without a predictive, coordinated way to intervene before customers canceled.
Delivered a 15 percent reduction in churn, roughly 60 percent less manual quality auditing effort, and a shift from reactive firefighting to proactive, prioritized account protection.
Built a mobile-first, AI-driven knowledge assistant that delivers validated answers through natural language chat, putting trusted service guidance directly into field technicians' hands at the moment of need.
A leading transport refrigeration and trailer provider had field technicians manually searching thousands of pages of third-party documentation, slowing resolution times and increasing the risk of errors.
Delivered 50 percent faster issue resolution, reduced dependency on subject matter experts, faster technician onboarding, and a scalable foundation for consistent service quality across locations.
Implemented GitHub Copilot across a tier 1 bank's engineering and QA teams to modernize a merchant onboarding portal, embedding AI-assisted development into refactoring, migration, and test automation.
A tier 1 U.S. bank needed to modernize its merchant portal onto a new stack, but complex code refactoring, framework migration, and rewriting automation test scripts created bottlenecks that slowed delivery.
Cut test automation development effort by 50%, lifted development productivity 126%, and improved code quality — establishing AI-enhanced workflows as a scalable foundation for future innovation.
