Agent Engineering HQThe home of the agent engineering discipline. Technical events, media, and a conference for engineers building production AI agents.

Writing effective prompts, system instructions, and prompt patterns. Covers structured outputs, few-shot design, prompt optimization, and LLM-native communication.

Shaping what an agent sees at runtime. Covers context construction, compression, grounding, retrieval, and MCP (Model Context Protocol).

Defines the execution environment around an agent. Wires models to tools, policies, sandboxes, and execution constraints.

Measuring agent behavior in non-deterministic systems. Covers evaluation frameworks, behavioral testing, LLM-as-judge, and reliability benchmarks.

How agents store, retrieve, and evolve state over time. Covers short-term vs long-term memory, retrieval strategies, and personalization.

Serving models efficiently in production. Covers GPU/hardware selection, inference engines (vLLM, SGLang, TensorRT-LLM), latency budgeting, batching, and cost optimization.

Designing the agent's run loop: the iterate, observe, and act cycle. Covers control flow, stopping conditions, retries, self-correction, and long-running autonomous loops.

Automating the entire software development lifecycle and engineering with AI agents.

Improving the coding capabilities of models and agents. Covers agentic coding, coding agents, code generation quality, and AI-assisted software development.

Standing up the protocol layer agents talk over. Covers MCP, ACP, A2A, A2UI, transport and auth choices, versioning, and cross-vendor interoperability.

Building agents with graphs. Covers graph databases, knowledge graphs, graph retrieval, and modelling agent state and execution as graphs.

Agentic search techniques and the retrieval layer underneath them. Covers query planning, ranking, hybrid search, and improving agent behaviour through better search.