Developer shares best practices from building 6 agent harnesses in 6 months
A developer recounts building six agent harnesses over six months and distills best practices from companies like Ramp, Stripe, OpenAI, and Anthropic. Key takeaways include using small agent prompts, deterministic gates, isolated environments, and managing state.
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Harness engineer reports easy creation of complex AI agents for multi-step automation
A harness engineer on Reddit describes how they can now create agents in hours that automate long, multi-step workflows, including generating an AI video series where each character is sourced from five different models. The post highlights the growing accessibility of agentic AI for practical automation.
Building Core Agent Behavior and Capabilities: Four Disciplines for Reliable Agents
A post outlines the four co-equal disciplines for building reliable AI agents: orchestration, tools, guardrails, and model behavior tuning. The key lesson from 2023–2026 is to start with the simplest architecture and add complexity only when evaluations demand it.
Runeward: Sandbox AI agents with policy gates
Runeward is a sandboxing tool for AI agents that enforces policy gates to restrict agent actions. It uses LLMs to interpret and enforce user-defined policies, solving the problem of unsafe or unintended agent behavior for developers building autonomous AI systems.
Community discusses agent reliability: Fix the loop, not the LLM
A series of Reddit posts and articles highlight that the main challenge in building reliable AI agents is architectural, not model quality. Practitioners share experiences where agents skip safety steps or hallucinate actions, advocating for structured loops with self-reflection, approval gates, and stop reasons. NVIDIA's Nemotron post-training data and a Medium guide reinforce that improving the agent loop—rather than upgrading the LLM—is key to production reliability.
Developer open-sources agent-instructions repo to curb AI coding agent degradation
A developer frustrated by AI coding agents losing context and hallucinating after about 10 minutes created a set of rules to keep them on track. The rules, shared as an open-source GitHub repo, aim to reduce the need for constant reminders and prevent infinite loops. The project has gained attention from other developers facing similar issues.