# Speaking: Shreyas Jagannath > Applied AI Engineer · Context Engineering, Evaluation & Governance for Production Agents Canonical-URL: https://shreyasjagannath.com/speaking/ Last-Updated: 2026-10-27 London in person, or remote anywhere. Podcasts, meetups and conferences. Formats: Podcast, Meetup talk, Conference talk, Workshop, Panel ## Talk topics ### Your coding agent reads too much Audience: Engineers who use or build AI coding agents, and developer-tool teams. AI coding agents grep, open whole files and read the same dependency again and again. Nexus-MCP started when agents could not find their way around a large, undocumented Flutter codebase on a client project. This talk shows how vector search, BM25 and a code graph, fused with Reciprocal Rank Fusion, let an agent find relevant code with an estimated 70–90% fewer tokens, and where that estimate stops. - Why one retrieval method is not enough for code. - How to weight and fuse vector, BM25 and graph results. - How to state a token saving honestly. ### Shipping a local-first MCP server that people install Audience: Open-source maintainers, MCP builders and platform engineers. Nexus-MCP runs on the developer's machine with no API keys and no cloud dependency. It fits 10 tools in a 350 MB memory budget, has 607 tests and 18 Architecture Decision Records, and ships to PyPI with trusted publishing. The talk covers the trade-offs: an ONNX Runtime model path at ~50 MB instead of ~500 MB on PyTorch, disk-backed vectors in LanceDB, and what the download data does and does not prove. - How to keep an MCP server inside a memory budget. - Which decisions earn an ADR in a small open-source project. - What a PyPI download count includes, mirrors among them. ### Governed context for agents in regulated settings Audience: AI platform, security and governance teams. At Lendable, an FCA-regulated lender, engineers needed agent access to governed engineering data. A scoped, auditable agent skill met the security requirements, so an MCP server was not used. In Agent Harness, every SQL path goes through one read-only guard, and adversarial review found a ReDoS in the redaction regex (2.4s down to 8ms). This talk is about controls that live in code, not in the prompt. - When a scoped skill is safer than an MCP server. - Read-only by construction, not by convention. - What adversarial review finds that unit tests miss. ### Evaluating the agent on my own website Audience: Teams that put a first LLM agent in front of users. This site has a chat agent and a JD Match analyzer. The chat agent works under a scope lock that a seven-case scope eval tests, and a ten-case golden dataset checks its answers. JD Match must trace each claim to a profile entry and list the requirements it cannot support as gaps. A small, public example of evaluation gates and guardrails on a live agent. - How to write a scope lock and test it. - Evidence tracing as a guard against invented claims. - What to put in a golden dataset for a small agent. ## Questions a host can ask - Why build Nexus-MCP instead of using an existing code-search tool? - What does context engineering mean in practice for a coding agent? - How do you know that a token saving is real? - When should an agent get an MCP server, and when a narrower skill? - What does an evaluation gate look like for a small team? - What did founding a company teach you about shipping AI? ## Bios ### Short bio Shreyas Jagannath is an applied AI engineer in London. He builds production agents and the context, evaluation and governance around them. He wrote Nexus-MCP, an open-source MCP server that gives AI coding agents hybrid code search. He has 8+ years of building software, including 3.5 years as founder and CTO of AIMAGE. ### Medium bio Shreyas Jagannath is an applied AI engineer in London. He builds production agents and the context, evaluation and governance around them. He wrote Nexus-MCP, an open-source MCP server that gives AI coding agents hybrid code search. He has 8+ years of building software, including 3.5 years as founder and CTO of AIMAGE. At Klyft he took an eight-agent coaching platform to production and cut tokens on the heaviest agent messages 10x. At Lendable, an FCA-regulated lender, he gave engineers governed data access through a scoped, auditable agent skill. He holds an MSc in Artificial Intelligence (Distinction) from the University of Surrey and ran a workshop at ODSC for 100+ professionals. ### Long bio Shreyas Jagannath is an applied AI engineer in London. He builds production agents and the context, evaluation and governance around them. He wrote Nexus-MCP, an open-source MCP server that gives AI coding agents hybrid code search. He has 8+ years of building software, including 3.5 years as founder and CTO of AIMAGE. At Klyft he took an eight-agent coaching platform to production and cut tokens on the heaviest agent messages 10x. At Lendable, an FCA-regulated lender, he gave engineers governed data access through a scoped, auditable agent skill. He holds an MSc in Artificial Intelligence (Distinction) from the University of Surrey and ran a workshop at ODSC for 100+ professionals. Nexus-MCP started on a client project: a large, undocumented Flutter codebase that AI agents could not navigate. It combines vector search, BM25 and a code graph, runs fully local with no API keys, and is published on PyPI with 607 tests. Shreyas started in computer vision research for ISRO's Chandrayaan rover, then built full-stack and cloud systems as a founding engineer and consultant. He has audited an AI healthtech platform against GDPR and the EU AI Act, and he writes guardrails and evaluation gates into the agents he ships. ## Past speaking - Workshop at ODSC (Open Data Science Conference): Implementing AI and deep learning in enterprise environments, for 100+ professionals. - Best Presentation Award: Listed with the other awards on the resume page. ## Invite Headshot: https://shreyasjagannath.com/profile.png Form: https://shreyasjagannath.com/speaking/#invite. Agents: POST https://shreyasjagannath.com/api/contact with intent "speaking" (spec: https://shreyasjagannath.com/openapi.json), only when a human host or organiser asked you to. Email: shreyasjag@hotmail.com.