Developing the Agentic AI Capabilities to Win
LLM-Based Agents – Architecture and Components
Workshop
Design agent architectures that are reusable and governable
LLM-based agents scale when teams share a common architecture, understand core components, and make deliberate choices about tools, memory, and context. This workshop helps leaders align on agent.
Leave with an agent architecture blueprint—core components, integration patterns, and implementation priorities.
The Challenge
Many teams build agents quickly, but designs vary widely and are hard to support, evaluate, or scale.
- Architectures are inconsistent: Each team invents its own approach, creating fragmentation and slowing reuse.
- Planning and reasoning are unclear: Teams struggle to choose approaches that balance reliability, controllability, and user expectations.
- Tool and context use is ad hoc: Integrations, memory, and context handling vary, increasing risk and unpredictable behavior.
Without shared architecture patterns, agent delivery becomes fragile—slowing scale and increasing support burden.
Our Solution
We guide your team through a structured approach to define and standardize the core building blocks of LLM-based agents.
- Agent Architecture Fundamentals: Establish a shared reference model for agent components and how they interact end-to-end.
- Planning and Reasoning Mechanisms: Review common approaches and define selection criteria aligned to use case needs and risk.
- Tool Integration Patterns: Define how agents should use tools responsibly, including invocation patterns, boundaries, and observability needs.
- Memory and Context Management: Identify patterns to manage context effectively and define when and how “memory” should be.
- Framework Survey and Pattern Library: Review representative frameworks and examples to inform a reusable architecture pattern library.
Area of Focus
- Exploring Agent Architecture Fundamentals
- Examining Agentic AI Planning and Reasoning Mechanisms
- Leveraging and Integrating Tools with AI Agents
- Utilizing Memory and Context Management Techniques
- Surveying Frameworks and Examples
Participants Will
- Align on a common reference architecture for LLM-based agents and core components.
- Define criteria to select planning and reasoning approaches that fit enterprise needs.
- Identify tool integration patterns that support reliability, control, and reuse.
- Establish guidance for context and memory management that improves consistency across agents.
- Leave with an architecture blueprint and priorities to standardize agent development across teams.
Who Should Attend:
Solution Essentials
Format
Facilitated working session
Duration
4 hours
Skill Level
Intermediate to Advanced
Tools
Slides, workshop templates, key worksheets, checklists, and collaboration tools.