You have built a few agents, wired them into a multi-agent system. What comes next? This workshop takes a focused look at one of the most critical and often underestimated parts of an agentic system — memory. Participants will come away with a practical understanding of how memory shapes agent behavior, capabilities, and user experience.
Format: Hands-on workshop
Duration: 4 hours
A quick grounding in agentic system fundamentals — core components (LLM, Tools, Planning, Execution, Memory), the agent execution lifecycle (Observe, Reason, Act, Reflect), and why memory is a first-class concern rather than an afterthought.
What actually breaks when an agent has no state: loss of context within a conversation, no personalisation, repetitive interactions.
Hands-on Exercise 1: Build a stateless travel assistant, run it through scenarios, and identify what’s missing.
Two practical patterns: using workflow state to store preferences, tool outputs, and intermediate decisions; and passing conversation history in the prompt to maintain continuity.
Includes a section on managing conversation history efficiently — what to preserve, what to drop, and common strategies for keeping history useful without letting it balloon.
Checkpoints, why they’re needed, and the LangGraph-specific concepts required: checkpointers, state persistence, workflow resumption, and interrupt-and-resume patterns.
Hands-on Exercise 2: Extend the travel assistant to retain budget, interests, destinations, and trip duration across a session, asking intelligent follow-ups and maintaining coherent context.
Limitations of short-term memory, when long-term memory becomes necessary, and the three types — semantic, episodic, and procedural. Architectures for how memory is created, stored, and retrieved, plus production best practices.
Hands-on Exercise 3: Extend the assistant further so it recalls preferred destinations, remembers budget ranges, recommends based on past choices, and picks up conversations across sessions.
Real trade-offs between memory approaches, common production challenges, and Q&A.
By the end of this workshop, participants will:
- Understand the role of memory in agentic systems
- Build stateless, session-aware, and persistent agents
- Implement short-term and long-term memory patterns
- Use checkpoints effectively in workflow orchestration
- Developers and engineers building AI-powered applications who want reliable, agent-driven workflows
- ML/AI engineers exploring agentic architectures
- Tech leads and solution architects designing scalable, multi-agent systems and automation platforms
- Platform and backend engineers integrating LLMs with real-world systems, APIs, and infrastructure
- Innovation, R&D, and product teams experimenting with AI agents, orchestration frameworks, and MCP-based extensibility
The instructors will start with this talk to get the fundamentals, then join the workshop for a hands-on deep dive into memory architectures for agentic systems.
Swetha A is a software developer and GenAI enthusiast, Solution Consultant at Sahaj Software. She builds intelligent systems using deep learning and generative AI, with a research paper presented at the International Conference on Data Analytics and Management. Has delivered talks on AI agents and facilitated workshops on AI-assisted SDLC and multi-agent systems.
Mahita D is a software developer focused on agentic systems and the evolving role of AI in software development. Shares experiences building agentic systems and helping teams turn AI capabilities into practical solutions through talks and workshops.
This workshop is open to:
🎟️ Fifth Elephant community members — https://hasgeek.com/fifthelephant#memberships
🎟️ Ticket holders for The Fifth Elephant annual conference — https://hasgeek.com/fifthelephant/enterprise-ai-in-production-meetup#tickets
This workshop is open to 30 participants (in-person) & hybrid access for remote attendees. Seats for in-person participants will be available on first-come-first-served basis. 🎟️
☎️ Call: (91) 7676332020
📧 Email: info@hasgeek.com