Context engineering

Oct 06, 2026

The 6 Pillars of an Agentic Harness for Production — Varun Krovvidi, Resolve AI

Varun Krovvidi of Resolve AI explains why AI excels at code generation but struggles with the complex, multi-domain problem of running and fixing software in production. He outlines five common failure modes for AI agents in production (anchoring bias, context issues, coherent vs. causal answers, guardrails, lack of learning) and introduces Resolve AI's six-pillar architecture: model orchestration, context engineering, causal reasoning, governed actions, learning systems, and multi-level evaluations. A demo illustrates how Resolve AI diagnoses a Grafana alert to its root cause, ruling out coincidental events and maintaining its causal reasoning.

Oct 02, 2026

MCP Doesn't Suck. Your Agent Does. — Jan Čurn, Apify

Jan Čurn, founder and CEO of Apify, argues that the backlash against MCP targets the wrong layer. Context bloat and token burn come from naive agent harnesses, not the protocol. He reviews the industry's three main fixes (sub-agents, progressive tool discovery and Code Mode), explains why agents work well with CLIs, proposes MCP for remote access and CLIs for local use, and demos mcpc, Apify's open-source universal MCP CLI client, alongside early Connector Evals results.

Sep 27, 2026

Orchestras, Not Factories: How the Fastest Builders Work — Charlie Holtz, Conductor

Charlie Holtz, co-founder and CEO of Conductor, shares six principles he has observed among the best AI-assisted builders, including staying near the frontier, creating slop-free zones, and giving agents cloud sandboxes. He also demos real-time collaboration and phone-initiated agents, and argues for orchestras over factories, with humans at the center of AI-driven development.

Sep 27, 2026

What It Actually Takes to Build a Software Factory — Tereza Tížková, Factory

Tereza Tížková of Factory explains what a software factory really is: an autonomous, end-to-end software lifecycle that goes far beyond coding agents. She covers three guiding principles of agnosticism, autonomy, and continuous improvement, including automatic model routing, long-running multi-agent missions with sequential workers and independent validators, a deferred context engine, and agent readiness checks, and closes with a view on what remains for humans.

Sep 14, 2026

We let an AI agent execute Bash and lived to talk about it — Sarah Sanders, PostHog

Sarah Sanders, a context engineer at PostHog, discusses the security implications of building an agentic CLI tool, "The Wizard," which automates developer workflows. She highlights the inherent risks of agents capable of executing commands, labeling it a "malware starter pack," and details how PostHog evolved its security posture from basic allow lists to a sophisticated "defense in depth." Key insights include the principle that "attacks compose, code review doesn't," the discovery of internal supply chain vulnerabilities through the "Context Mill," and the development of "The Warlock," a deterministic scanner using YARA rules. Sanders emphasizes the critical distinction between deterministic enforcement and probabilistic judgment, explaining how an LLM-powered "Triage" layer acts as an adviser (failing closed) rather than a bouncer, preserving the integrity of their security model.

Sep 09, 2026

Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked

Brandon Waselnuk of Unblocked discusses the critical need for robust context engines in AI-driven software development. He explains how AI agents' lack of company-specific understanding leads to compounding costs like wasted tokens and rework. Waselnuk outlines common pitfalls in providing context and details six essential characteristics of an effective context engine, demonstrating its impact with a 50% token reduction and significant time savings. He also introduces three open-source tools from Unblocked, including a workshop to build a relational context engine, emphasizing that "access to information is not understanding" and that context, not intelligence, is the current bottleneck for AI effectiveness.