FDAI Launches Enterprise Implementation Support for AI Agents Utilizing Managed Agents
FDAI has begun offering an implementation support service for corporate AI agents utilizing Anthropic's 'Claude Managed Agents'. The service comprehensively supports everything from business requirement design and permission control to system implementation and operational integration.
📋 Article Processing Timeline
- 📰 Published: April 11, 2026 at 06:14
- 🔍 Collected: April 11, 2026 at 00:19
- 🤖 AI Analyzed: April 20, 2026 at 07:59 (223h 39m after Collected)
ForwardDeploy AI Inc. (Headquarters: Shibuya-ku, Tokyo, CEO: Kazuto Maruoka, hereinafter FDAI) has launched enterprise implementation support services utilizing 'Claude Managed Agents' provided by Anthropic.
Claude Managed Agents is a managed agent execution platform suited for long-running processes, multi-step executions, and continuous stateful operations. To smoothly integrate this platform into corporate operations, FDAI provides end-to-end support, from organizing usage policies and business design to permission control, system implementation, and operational integration.
[Background of Service Launch]
The business use of Generative AI is expanding from simply answering questions via chat to utilizing continuously operating agents within daily workflows. However, in actual implementation, it is essential to design what information the agent can access, the extent of automation, who approves actions, and how histories are logged.
While Claude Managed Agents provides a managed execution base making long-running and asynchronous processing easier, companies still need to individually design and implement permission controls, auditability, and integration with existing systems according to their own rules. FDAI launched this service to support these final crucial steps of implementation.
[Service Details]
This service mainly provides the following:
- Organizing usage purposes and target tasks
- Agent design tailored to business requirements
- Permission design per user and department
- Design of approval flows, audit logs, and operational rules
- Integration design with internal data and various SaaS
- Implementation support from PoC to production
- Post-implementation improvement and integration support
FDAI focuses not on creating 'working demos,' but on building mechanisms that can be used continuously in the field. They design systems that include logs traceable by administrators, easily reviewable structures, and clear demarcations of responsibility during operation, aiming for a state where companies can use AI with peace of mind.
[Expected Use Cases]
- Agents that cross-sectionally analyze marketing data and propose improvement measures
- Agents that accumulate research results, organize them by department, and link them to proposals
- Agents that draft reports and documents while referencing various internal and external data
- Agents that semi-automate parts of routine tasks while incorporating human approvals
[FDAI's Value Proposition]
FDAI emphasizes not merely introducing new tools, but shaping AI agents into a form that can be safely and continuously used within a company. They proceed without separating business design from system design, implement based on the premise of duty segregation and approval flows, ensure that the rationale and history of proposals can be verified later, and build architectures that are adaptable to future changes in models or execution platforms.
Claude Managed Agents is a managed agent execution platform suited for long-running processes, multi-step executions, and continuous stateful operations. To smoothly integrate this platform into corporate operations, FDAI provides end-to-end support, from organizing usage policies and business design to permission control, system implementation, and operational integration.
[Background of Service Launch]
The business use of Generative AI is expanding from simply answering questions via chat to utilizing continuously operating agents within daily workflows. However, in actual implementation, it is essential to design what information the agent can access, the extent of automation, who approves actions, and how histories are logged.
While Claude Managed Agents provides a managed execution base making long-running and asynchronous processing easier, companies still need to individually design and implement permission controls, auditability, and integration with existing systems according to their own rules. FDAI launched this service to support these final crucial steps of implementation.
[Service Details]
This service mainly provides the following:
- Organizing usage purposes and target tasks
- Agent design tailored to business requirements
- Permission design per user and department
- Design of approval flows, audit logs, and operational rules
- Integration design with internal data and various SaaS
- Implementation support from PoC to production
- Post-implementation improvement and integration support
FDAI focuses not on creating 'working demos,' but on building mechanisms that can be used continuously in the field. They design systems that include logs traceable by administrators, easily reviewable structures, and clear demarcations of responsibility during operation, aiming for a state where companies can use AI with peace of mind.
[Expected Use Cases]
- Agents that cross-sectionally analyze marketing data and propose improvement measures
- Agents that accumulate research results, organize them by department, and link them to proposals
- Agents that draft reports and documents while referencing various internal and external data
- Agents that semi-automate parts of routine tasks while incorporating human approvals
[FDAI's Value Proposition]
FDAI emphasizes not merely introducing new tools, but shaping AI agents into a form that can be safely and continuously used within a company. They proceed without separating business design from system design, implement based on the premise of duty segregation and approval flows, ensure that the rationale and history of proposals can be verified later, and build architectures that are adaptable to future changes in models or execution platforms.