Peter

Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked

May 3, 2026 · 1:41:25

Peter Werry of Unblocked argues that context engines—systems that supply AI agents with only the relevant organizational context—are critical to avoid agent doom loops and wasted tokens. He debunks three myths: naive RAG, connecting MCP servers, and bigger context windows do not solve the context problem. Werry describes building a social engineering graph to identify experts and distill team best practices, and shares hard lessons including hiding conflicts and caching answers. In a benchmark task, Unblocked's context engine reduced a 2.5-hour, 21-million-token task to 25 minutes and 10 million tokens. The talk offers a practitioner's guide to building context engines with conflict resolution, personalization, and access control.

Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel

Jan 6, 2026 · 1:09:49

Peter Wielander from Vercel introduces the Workflow DevKit, an open-source library that adds durability, observability, and resumability to AI agents by wrapping them in a workflow pattern. He demonstrates converting a coding agent into a workflow-supported agent, showing how steps like LLM calls and tool executions become isolated, retryable units. The toolkit enables long-running agents that can sleep for days, resume streams after disconnection, and integrate human-in-the-loop via webhooks. Deployable on Vercel or any cloud, it provides built-in observability through a local UI. The Workflow DevKit is currently in beta with general availability targeted for January.

Shipping an Enterprise Voice AI Agent in 100 Days - Peter Bar, Intercom Fin

Jul 18, 2025 · 17:10

Peter Bar, Product Lead at Intercom, details the 100-day build of Fin Voice, an AI voice agent for enterprise phone support. The agent handles knowledge-based queries using a stack of speech-to-text, LLM, text-to-speech, RAG, and telephony, achieving ~1 second latency for simple queries and using filler phrases for longer ones. Key product decisions included focusing on out-of-office hours as an initial wedge, designing conversations for voice differences like answer chunking, and prioritizing integration with human support workflows over model improvements. The team measured success via resolution rate (user confirming resolution or not calling back within 24 hours) and used an LLM-as-judge for quality analysis. Bar argues that voice AI is the next frontier in customer service, citing cost reduction from $7–12 per human-handled call to 3–20 cents per minute with AI.

Which Jobs Can Be Replaced Today: Fryderyk Wiatrowski and Peter Albert

Feb 6, 2025 · 19:59

Fryderyk Wiatrowski and Peter Albert, co-founders of Zeta Labs, argue that autonomous browser agents will first replace reactive jobs—like customer support and scheduling—by automating low-leverage tasks while preserving human focus on high-leverage activities. They propose a trigger-pool system where agents react to emails, Slack, or events, requiring only approval for actions. Peter details building reliable agents: start with prompting optimized for model distribution, then add cognitive architectures (e.g., planning, scratchpads) to split tasks, and finally fine-tune with synthetic data or reinforcement learning. He advises minimizing noise in prompts, preferring text-based reasoning over images, and using language model judges to filter training data. The founders see continuous model improvement enabling agents to handle increasingly complex, proactive roles, moving toward full job replacement.

Mastering LLM Inference Optimization From Theory to Cost Effective Deployment: Mark Moyou

Jan 1, 2025 · 33:39

NVIDIA solutions architect Mark Moyou explains that LLM inference differs fundamentally from standard deep learning deployment, requiring careful management of KV Cache, attention mechanisms, and GPU memory to control cost. He details how tokens are processed: prefill computes attention across the entire prompt, then generation produces one token at a time, with KV Cache storing key-value pairs to avoid recomputation. Llama's 32 attention heads and FP8 quantization (halving memory with near-identical accuracy) are cited as key optimizations. Moyou emphasizes measuring time to first token, inter-token latency, and input/output sequence length distributions to size inference engines. He presents NVIDIA's TRT-LLM (model compilation for LLMs) and Triton inference server as tools to maximize throughput, and discusses how query patterns like long-input-short-output or short-input-long-output impact GPU utilization and deployment cost.