Zero code generation
Deterministic configuration makes change safer, testing simpler, and auditability stronger in production.
- Controlled changes and traceability by design
- Predictable behavior in mission critical environments
Processing...
Enterprise Grade No-Code
Native, Governed AI
Legacy Apps Refactoring
Build and modernize mission-critical systems, without losing control.
Deterministic configuration for delivery, native agentic AI for control and productivity, and legacy refactoring to modernize safely.
Build enterprise applications faster through deterministic configuration, with governance and auditability by design.
- ✓ Model workflows and business rules
- ✓ Design portals and screens
- ✓ Operate with monitoring and diagnostics
Configure governed AI agents inside the platform to control, validate, score, and assist users in real processes.
- ✓ Orchestrated agents with execution boundaries
- ✓ Native similarity and scoring for controls
- ✓ Agents for document and data validation
Make legacy systems readable and structured to modernize safely, without blind rewrites.
- ✓ Extract rules and knowledge
- ✓ Map dependencies and flows
- ✓ Accelerate governed modernization
Why Works Platform
Deliver and evolve mission critical systems faster, without losing governance.
◫
Manage complexity at scale
A modular, loosely coupled architecture to assemble capabilities and evolve solutions without rewrites.
- Reusable building blocks across apps and teams
- Enterprise-grade flexibility through configuration
◈
AI you can govern
Configure agents inside the platform to control, validate, score, and assist users with clear execution boundaries.
- Agent orchestration with full traceability
- Native similarity and scoring for controls
◎
Enterprise-ready operations
Built-in foundations for running at scale: monitoring, diagnostics, audit, and multi-language support.
- Production observability and troubleshooting
- Governance aligned with enterprise policies
Proven in complex environments
Used to deliver enterprise systems across ports and logistics, banking, public sector, and social security.
Ports and logistics
Banking
Public sector
Social security
How Works delivers in enterprise
A practical, governed path from modeling to production operations, then continuous evolution.
1
▦
Model
Configure data, screens, workflows, and business rules with deterministic behavior.
- Reusable building blocks
- Clear governance and validation
2
⧉
Integrate
Connect to existing systems using enterprise standards and your internal policies.
- Aligns with your architecture
- No forced redesign
3
◷
Deploy and operate
Run at scale with monitoring, diagnostics, logs, and audit readiness built in.
- Production observability
- Traceability and control
4
⌁
Evolve safely
Deliver changes continuously through configuration, with governed AI where it helps.
- Deterministic change governance
- Configured agents with execution boundaries
Use cases for complex, mission-critical systems
Pick your domain. Each use case combines deterministic delivery, governed AI agents for control and validation, and structured modernization when legacy constraints exist.
⚓
Ports and logistics
MISSION CRITICAL OPERATIONS
Port Community and Freight Systems
Orchestrate complex multi-actor workflows, with traceability and controlled evolution across years.
- Process orchestration and exceptions
- Cross-actor portals and controlled access
- Governed agent controls for validation
- Legacy modernization without disruption
Platform
Agentic AI
Legacy Refactoring
🏦
Banking and financial services
GOVERNANCE AND COMPLIANCE
KYC, Data Quality, and Controls
Standardize processes and embed governed AI agents for scoring, anomaly detection, and validations.
- Rules-driven customer lifecycle workflows
- Agents for document and data validation
- Similarity and scoring for controls
- Full traceability with human validation
Platform
Agentic AI
Legacy Refactoring
🏛️
Public sector and regulatory
POLICY-DRIVEN SYSTEMS
Regulatory Platforms and Case Tracking
Build long-lifecycle systems with governed change, auditability, and secure collaboration.
- Structured workflows and approvals
- Portals, forms, and document templates
- Operational monitoring and traceability
- Modernize safely with structured refactoring
Platform
Agentic AI
Legacy Refactoring
🧩
Social security
HIGH VOLUME PROCESSES
Benefits, Claims, and Eligibility
Industrialize processes with rules and traceability, while keeping change governance under control.
- End-to-end workflow automation
- Rules-driven eligibility and decisions
- Agents for validation and consistency checks
- Controlled modernization over time
Platform
Agentic AI
Legacy Refactoring
📄
Claims management
DOCUMENT-HEAVY OPERATIONS
Claims Processing and Controls
Manage complex claims workflows with evidence, traceability, and governed AI-assisted controls.
- Structured workflows and SLA tracking
- Document templates and DMS patterns
- Agents for document control and validation
- Audit-ready operations and monitoring
Platform
Agentic AI
Legacy Refactoring
⚖️
Legal and CASE-style
LONG LIFECYCLE CASEWORK
Case Management and Litigation
Model complex cases, processes, and documents with controlled evolution and full traceability.
- Case workflows and decision traceability
- Document generation and structured evidence
- Agents for control, validation, and scoring
- Modernize safely if legacy constraints exist
Platform
Agentic AI
Legacy Refactoring
Agentic AI
Configure governed AI agents
inside your enterprise systems
Works Platform makes agentic AI operational and controllable. Agents can be designed for validation, scoring, similarity checks, and anomaly detection, then embedded directly into workflows and applications with clear execution boundaries and full traceability.
Agents embedded in processes
Governance and traceability
Scoring, similarity, validations
What it is
What “Agentic AI” means in Works Platform
Agentic AI in Works is designed for enterprise control. Agents are configured capabilities that execute inside the platform, aligned with governance, permissions, and traceability. They assist people and processes, without removing accountability.
Agents are governed capabilities, not open-ended bots
An agent has a defined purpose, allowed actions, and controlled data scope. It runs within Works Platform rules, roles, and approval patterns so outcomes remain predictable and auditable.
- Explicit boundaries: purpose, inputs, outputs, and allowed actions
- Permissioned execution aligned with roles and governance policies
- Traceable outputs: what was used, what was produced, and what was validated
Agents are embedded into processes, not isolated features
Agents can be attached to workflows, screens, validation steps, and background controls. They enhance how teams score, compare, validate, and remediate, while keeping human validation where required.
- Inside workflows: approvals, checks, exceptions, and remediation
- Inside applications: guided user support and assisted validation
- Control patterns: similarity, scoring, anomaly detection, and review queues
Enterprise AI interoperability
MCP support
A2A support
MCP support (agent tooling alignment)
Works agents can be connected to approved tools and resources through structured tooling patterns, so capabilities stay controlled, auditable, and easy to govern at scale.
Practical outcome: connect agents to enterprise resources while keeping a clear boundary between “what the agent can do” and “what it cannot”.
A2A support (agent-to-agent orchestration)
Works supports coordinated agents where specialized agents collaborate under orchestration rules. This enables complex control scenarios: one agent detects, another validates, another routes, all within governed execution.
Practical outcome: build multi-agent controls for document checks, data validation, scoring and remediation, without losing accountability.
Design principle
Agentic AI is valuable only when it is operational, controlled, and accountable. Works Platform turns AI into governed capabilities that fit real enterprise processes, with traceability and human validation where needed.
How it works
The agent lifecycle
Agentic AI becomes enterprise-ready when it is designed, validated, operated, and improved through controlled steps. Works Platform supports this lifecycle so agents can scale safely across teams, use cases, and years.
01
Purpose
Design
Define what the agent is for and what “success” looks like.
- Target outcome and decision boundaries
- Inputs, outputs, and evidence required
02
Guardrails
Configure
Attach the agent to the right process steps and permissions.
- Allowed actions and data scope
- Triggers, workflow steps, and exceptions
03
Confidence
Validate
Test behavior against your rules and operational constraints.
- Human validation and approval paths
- Controlled rollout through environments
04
Operations
Run
Operate with traceability, monitoring, and accountable execution.
- Traceable inputs, outputs, and decisions
- Exception handling and escalation rules
05
Evolve
Improve
Iterate safely with governed changes and measurable control gains.
- Controlled updates and regression checks
- Scale by reusable agent patterns
Key takeaway
This lifecycle turns agentic AI into an industrial capability: fast to deploy, safe to operate, and structured to improve over time.
Enterprise control
Built-in agent controls
Works Platform is designed so AI agents can be deployed safely: controlled scope, permissioned actions, human validation paths, and traceability that fits governance requirements.
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Permissioned actions
Agents execute only what is explicitly allowed: read, suggest, validate, route, or raise exceptions, based on roles and policies.
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Workflow embedding
Agents are attached to defined steps: checks, approvals, data completion, document review, or exception handling, not ad-hoc automation.
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Human validation by design
Agents can propose and prioritize, while final decisions remain with people when required, with structured review queues and approvals.
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Traceability and monitoring
Keep a clear record of what the agent used, what it produced, and what was validated, with operational monitoring for continuous control.
Fit your ecosystem
Connect agents to your enterprise environment
Agents become operational when they can work across your existing applications, data, and policies. Works Platform is designed to integrate and align with enterprise standards, so you can activate agents without reshaping your architecture.
Designed to align with what you already run
Works Platform can connect to your systems and data sources through enterprise patterns and controlled interfaces. This allows agents to validate, score, route, and assist, while keeping authority and governance within your environment.
Existing applications and services
Identity and access policies
Data governance and validation processes
Audit and monitoring requirements
Your environment
Existing systems, data, processes, and policies
Works agents
Controlled actions embedded into workflows and applications
Important note
Integrations depend on the availability of compatible interfaces from external systems. Works Platform is designed to connect through enterprise standards and controlled interfaces, so alignment is typically straightforward when those interfaces are available.
Production-ready
Operate Agentic AI with control, not hope
In Works Platform, agents are not “special features” on the side. They are operational capabilities that you can supervise, audit, and evolve like any other part of a mission-critical system.
Monitoring you can trust
Track agent activity in the same operational mindset as your workflows and applications: visibility first, then control.
- Execution trace: what ran, when, and in which business context
- Outcome visibility: suggested decisions, validations, and escalations
- Diagnostics: detect drift, exceptions, and operational bottlenecks
Human validation stays in the loop
Works agents can propose, score, and validate, but accountability remains with your teams through structured review patterns.
- Review queues: prioritize what must be checked and by whom
- Approval steps: gated actions for sensitive operations and decisions
- Full accountability: who accepted, overrode, or escalated an outcome
Evolve agents safely over time
Agent behavior is configured and governed. You can update what they do without turning production into an experiment.
- Controlled configuration changes with clear scope and impact
- Consistent behavior across environments through governed release patterns
- Reusable controls: apply the same agent pattern across many processes
Operations principle
Agentic AI becomes enterprise-grade when it is measurable, reviewable, and evolvable. Works Platform treats agents as governed operational capabilities, so you can run them in production with traceability and continuous control.