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AI systems need more than intelligence; they need context that persists. Without it, even strong models can misinterpret information, lose decision rationale, or repeat the same mistakes. Context Graphs have emerged as a practical pattern for agentic AI: a living graph that captures not only what was retrieved or known, but how context led to actions through tool calls, constraints, policies, and outcomes, stitched across entities and time so precedent becomes searchable.
This talk explores context engineering as the discipline of designing that context layer, and shows how context graphs complement retrieval by enabling multi-hop, structured context assembly (building on GraphRAG-style hierarchical summaries) while improving explainability and evaluation. Attendees will leave with a practical understanding of how to build context pipelines that combine contextual retrieval with persistent memory and provenance, and why context graphs are becoming central to trustworthy, enterprise-ready AI systems.
Model Context Protocol (MCP) is quickly becoming a foundational interface for agent–tool interaction, but most implementations today stop at simple, single-server tool exposure. This session explores practical MCP design patterns that move beyond “one server, one agent” toward scalable, interoperable, and ecosystem-friendly architectures.
Based on real-world experimentation and open-source implementations, we will walk through a set of MCP patterns, including: 1. Single MCP Server patterns for tool and data exposure 2. Multi-Server composition and routing patterns 3. MCP Host / Gateway patterns for aggregation and policy control 4. Plugin-style extension patterns that allow third-party MCP servers to integrate without code changes 5. Read vs. write MCP patterns for observability, automation, and feedback loops
The talk focuses on when and why to apply each pattern, common pitfalls, and architectural trade-offs. Attendees will leave with a mental model for designing MCP-based systems that scale from local experiments to ecosystem-level platforms, enabling agents, tools, and platforms to evolve independently while remaining interoperable.
As LLMs shift from chatbots to autonomous agents, the limits of "single-shot" RAG are surfacing. Static retrieval often introduces irrelevant context that misleads models. To solve this, developers are adopting Corrective (CRAG) and Adaptive RAG, requiring databases to act as active reasoning runtimes rather than simple stores.
This session explores building self-correcting AI agents using an open-source relational stack. We will demonstrate how to bridge the gap between semantic search and structured data using the Model Context Protocol (MCP) and native MariaDB vector indexing.
Technical topics include:
The Critic Loop: Implementing self-correcting architectures that validate retrieved documents before LLM synthesis.
Hybrid Querying: Combining vector indexing with relational SQL in single ACID transactions to reduce agentic loop latency.
Standardizing Communication: Using the MariaDB MCP Server for secure, tool-based access to live data.
Scaling State: Managing concurrent agent sessions without sacrificing data integrity.
Attendees will leave with a blueprint for building reliable, autonomous systems using open-source database patterns that move beyond basic vector search.
As a technologist and database expert, Ben Grieser works at the intersection of open source innovation and product engineering. In his role at MariaDB, he regularly talks with team using open source technology to bring complex data products to life. Ben is passionate about making... Read More →
Every enterprise has identity management for humans—SSO, MFA, RBAC, audit logs. But AI agents? They run with API keys, no verified identity, no behavioral tracking, no audit trail.
This talk bridges the gap between traditional IAM and the emerging world of autonomous AI agents:
What we learned from human IAM:
- Why identity must be cryptographic, not just credentials
- How least-privilege access control prevents lateral movement
- Why audit trails matter for compliance and incident response
Applying it to AI agents:
- Agent identity: Ed25519 keypairs vs API keys
- Capability-based access: what tools can this agent call?
- MCP server attestation: verifying the tools agents connect to
We'll examine real attack scenarios—agent impersonation, tool injection, privilege escalation—and show how identity-first security prevents them.
Live demo using AIM (Agent Identity Management), an Apache-2.0 open-source platform. All patterns are framework-agnostic and applicable to LangChain, CrewAI, AutoGen, or raw MCP implementations.
Attendees leave with actionable security patterns for their AI agent deployments.
Abdel is a cybersecurity architect with 17+ years of experience securing enterprise environments across healthcare, finance, and government sectors. He has led security initiatives at Grail, Booz Allen Hamilton, Protiviti, and Allstate, specializing in cloud security & DevSecOps. ... Read More →
LLM tokens are expensive. With context windows expanding to 200K+ tokens, a single API call can cost several dollars & in production systems handling thousands of requests, these costs compound quickly. Most optimization efforts focus on model selection or prompt engineering, but the context itself often contains massive redundancy.
Headroom is an open-source Python library (https://github.com/chopratejas/headroom) that sits between your application and your LLM provider, transparently optimizing context before it reaches the model. The core insight is simple: LLM contexts—especially in agentic workflows—are filled with repetitive tool outputs, verbose JSON arrays, and boilerplate that consumes tokens without adding proportional value
Headroom introduces novel concepts such as reversible compression, cache aligners, compression routers, and even persistent memory
Real-world results: - 50-90% token reduction on typical agentic workloads - Drop-in integrations for LangChain, OpenAI, Anthropic, and any OpenAI-compatible provider - Zero code changes required when using the proxy server
Tejas Chopra is a senior ML and AI infrastructure Engineer at Netflix, where he builds large-scale systems for production AI and data platforms. He is the creator of Headroom, an open-source context optimization engine for LLMs, and a frequent speaker at global conferences on ML systems... Read More →
