Agentic AI agents collaborate across functions to make your business more agile
Infor agents go beyond data analysis—they act on it, performing tasks and closing loops independently. Ideal for integrating workflows across finance, supply chain, HR, and more
Break down organizational silos as agents share context and coordinate actions in real time across CloudSuite modules
Infor agents autonomously plan and execute multi‑step tasks—from intent recognition to system interaction—using connected tools and APIs
Agents are trained on your domain‑specific data, powered by the Infor GenAI Knowledge Hub and CloudSuite metadata
Infor’s Agentic AI isn’t just reactive—they’re proactive
It’s the ability to plan, execute, and collaborate autonomously
Agents perceive context, plan actions, act via API connectors, and learn from feedback
With memory and feedback loops, agents self‑correct, refine, and improve based on past performance
Specialized agents coordinate modular tasks—each handling a piece of the process and sharing state to achieve complex goals
Architecture
Deploy purpose-built AI agents across critical sectors—from aerospace to retail—to automate decision-making, enforce compliance, and orchestrate real-time workflows at scale
Aerospace Agent
Automotive Agent
Food Agent
Fashion Agent
Industrial Agent
Construction Agent
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AI agents autonomously track evolving regulatory requirements and update operational parameters instantly. Detect and flag out-of-spec parts, notify MES systems, and trigger timely reorders to maintain compliance and minimize delays
Automate cross-departmental workflows with AI agents that coordinate engineering, manufacturing, and logistics. Enhance transparency and reduce risk across complex, high-stakes aerospace operations
AI agents analyze logistics data in real time, proactively rerouting materials or initiating supplier interactions. Maintain production schedules even under fluctuating demand or regulatory disruptions
Agents detect anomalies on production lines, recommend corrective actions, and validate specifications to ensure defect-free outputs—essential for aerospace-grade manufacturing
Enable faster compliance reporting with agents that collect, verify, and submit audit-ready data—freeing up engineering and operations teams for high-value tasks
AI agents detect real-time line slowdowns or inventory gaps and autonomously initiate adjustments—rerouting tasks or deploying additional agents for rapid resolution
Agents modify supply orders dynamically based on real-time consumption and predictive demand, improving part availability and production continuity
Agents act as real-time intermediaries between design, manufacturing, and procurement—ensuring changes are communicated instantly and incorporated into production
Replace delayed reports with AI agents that act on insights in the moment, from adjusting shift schedules to reallocating resources across production lines
Deploy intelligent agent networks that handle logistics, line management, quality control, and materials procurement in parallel—boosting overall throughput
AI agents monitor ingredient quality and batch ratios, stopping production or adjusting processes instantly when quality drifts from set thresholds
Agents detect quality degradation and autonomously switch to alternative suppliers, maintaining consistency and ensuring uninterrupted operations
Automate compliance tracking and ensure HACCP or ISO standards are met by continuously monitoring processing parameters and generating safety alerts
AI agents fine-tune mixing, temperature, or storage conditions to ensure taste and shelf-life targets are met, adapting instantly to ingredient variability
Predict spoilage, overproduction, or line malfunctions, and act proactively—reducing waste, ensuring freshness, and improving sustainability metrics
Agents monitor demand spikes in real-time and autonomously adjust production schedules, inventory allocations, and even marketing campaigns
Use AI agents to fast-track design-to-shelf workflows—automating design updates, material sourcing, and order routing based on demand signals
Automatically launch region-specific marketing based on trend detection and sales data—driving hyper-targeted engagement with minimal human input
AI agents detect low-stock alerts in key regions and reroute goods from underperforming markets—balancing demand and reducing overstocking
Connect inventory, sales, and customer feedback through multi-agent systems for real-time product personalization and faster product launches
AI agents monitor machine health and automatically reroute jobs to alternate lines when issues arise—ensuring no halt in output
Rather than alerting humans, agents detect early failures and autonomously schedule maintenance, order parts, and dispatch technicians
Monitor KPIs like cycle time and OEE with agents that dynamically reassign workloads to underused equipment—maximizing throughput
When anomalies occur, agents self-diagnose the root cause, alert relevant stakeholders, and execute corrective actions in real time
Coordinate production shifts with parts availability by triggering just-in-time deliveries or reordering directly from integrated vendor systems
AI agents assess weather, labor availability, and supply chain updates to autonomously revise timelines and prevent delays
Agents identify underutilized assets or labor and redeploy them between projects, improving on-site productivity and resource utilization
Track regulation updates and adjust workflows or notify legal and operations teams, ensuring continuous compliance across geographies
Automatically issue RFQs, monitor deliveries, and reschedule tasks based on material arrival delays—keeping everything on track without human intervention
Generate real-time updates for stakeholders with project progress, issue alerts, and revised forecasts based on agent-led schedule adjustments
Use Cases
Deploy AI agents with confidence—by embedding governance, oversight, and security protocols into every layer of your enterprise AI stack
Controlled Access and Monitoring
Treat AI agents like human users. Assign role-based privileges, restrict access to sensitive systems, and enable real-time monitoring and alerting to prevent misuse or unintended actions
Piloted and Measurable Use Cases
Begin with narrow, well-defined agent tasks in controlled environments. Define success metrics aligned with business objectives to validate impact before scaling across production systems
Governance Framework and Boundaries
Establish clear governance policies and accountability structures. Define agent responsibilities, enforce boundaries, and implement regular audits to ensure operational alignment and ethical performance
Bias Mitigation and Continuous Evaluation
Continuously monitor AI agent outputs for fairness and bias. Use diverse, representative training data and retrain models regularly to ensure accuracy, transparency, and ethical behavior
Human Oversight and Regulatory Compliance
Integrate human-in-the-loop protocols for high-impact decisions. Stay aligned with evolving AI regulations by embedding compliance policies and robust cybersecurity measures into data access and agent logic
Integrations
Build automations ready to scale at the click of a button and craft AI agents based on your goals
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Deploy purpose-built AI agents—each with unique strengths—crafted to scale intelligently across your enterprise operations
Introduce rule-based AI agents to execute fixed logic tasks with high consistency across structured business workflows
Automate Routine Tasks: Reduce manual validation by automating predictable, rules-driven operations
Enforce Process Compliance : Ensure compliance through repeatable processes with minimal variance
Deploy reactive AI agents that respond instantly to changing system inputs, ideal for time-sensitive environments
Accelerate Response Time : Eliminate latency in detection and response cycles across IT and security
Ensure Operational Continuity : Maintain business continuity through continuous input-driven actions
Incorporate learning-based agents that evolve through feedback and performance outcomes, enabling data-informed decisions
Self-Optimizing Systems : Continuously improve results without reprogramming logic
Dynamic Workflow Adaptation : Adapt workflows dynamically in fast-changing business environments
Implement interactive agents capable of contextual dialogue and intelligent task resolution across user touchpoints
Automated Issue Resolution: Deliver faster issue resolution without escalating to human teams
Context-Aware AI Support : Personalize enterprise service delivery with contextual AI engagement
Introduce rule-based AI agents to execute fixed logic tasks with high consistency across structured business workflows
Deploy reactive AI agents that respond instantly to changing system inputs, ideal for time-sensitive environments
Incorporate learning-based agents that evolve through feedback and performance outcomes, enabling data-informed decisions
Implement interactive agents capable of contextual dialogue and intelligent task resolution across user touchpoints