Large Language Model Agents (LLM Agents)
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- Agent-Oriented Architecture
- Agentic AI Alignment
- Agentic AI for Customer Engagement
- Agentic AI for Decision Support
- Agentic AI for Knowledge Management
- Agentic AI for Predictive Operations
- Agentic AI for Process Optimization
- Agentic AI for Workflow Automation
- Agentic AI Safety
- Agentic AI Strategy
- Agile Development
- Agile Development Methodology
- AI Actionability Layer
- AI Adoption & Strategy
- AI Adoption Framework
- AI Adoption Plans with Milestones
- AI Adoption Process
- AI Adoption Strategies with KPIs
- AI Agents for IT Service Management
- AI Applications
- AI Bias
- AI Change Management
- AI for Compliance Monitoring
- AI for Customer Sentiment Analysis
- AI for Demand Forecasting
- AI for Edge Computing (Edge AI)
- AI for Energy Consumption Optimization
- AI for Predictive Analytics
- AI for Predictive Maintenance
- AI for Real Time Risk Monitoring
- AI for Telecom Network Optimization
- AI Governance
- AI Governance Frameworks
- AI Implementation Approach
- AI Implementation Methodology
- AI in Cybersecurity
- AI in Education
- AI in Entertainment
- AI in Finance
- AI in Healthcare
- AI in Manufacturing
- AI in Marketing
- AI in Public Sector Service Delivery
- AI in Transportation
- AI Orchestration
- AI Performance Measurement (KPIs, ROI)
- AI Policy
- AI Research
- AI Risk Management Practices
- AI Safety
- AI Scalability Frameworks
- AI Strategy Alignment with Business Goals
- AI Thought Leadership
- AI Use-Case Discovery
- AI Use-Case Prioritization
- AI-Driven Business Transformation
- AI-driven cloud-native transformations
- AI-Driven Cybersecurity Solutions
- AI-driven Process Automation
- AI-Driven Supply Chain Optimization
- AI/ML
- Algorithm
- API Integration
- API Management
- Application Modernization
- Applied & GenAI
- Artificial Intelligence
- Artificial Neural Network
- Augmented Reality
- Autonomous AI Agents
- Autonomous Systems
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C
D
E
F
G
H
I
L
M
N
P
Q
R
S
T
V
W
At Xebia, Large Language Model Agents are AI systems built on top of powerful language models such as GPT that can reason, plan, and take actions to achieve specific tasks. Unlike static chatbots, LLM Agents combine natural language understanding with tool usage, memory, and decision making, allowing them to perform multi step workflows autonomously.
Xebia helps organizations design and deploy LLM Agents that integrate with enterprise data, APIs, and applications. By combining advanced prompting, orchestration frameworks, and governance, Xebia ensures that LLM Agents deliver reliable outcomes while maintaining compliance and security.
What Are the Key Benefits of Large Language Model Agents?
- Smarter automation of knowledge intensive tasks
- Context aware interactions that adapt to user needs
- Ability to integrate with enterprise tools and workflows
- Reduced operational costs through autonomous task execution
- Faster experimentation and innovation using reusable agent frameworks
- Enhanced productivity by combining reasoning, memory, and external tool use
What Are Some Large Language Model Agents Use Cases at Xebia?
- Customer service agents that resolve queries by accessing knowledge bases and ticketing systems
- Research assistants that summarize, cross reference, and synthesize large documents
- Software engineering copilots that debug, test, and generate code in context
- Financial analysis agents that monitor markets and produce real time reports
- Compliance and governance assistants that automate policy checks and documentation
- Workflow orchestrators that coordinate multiple applications through natural language commands
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