@vintcessun: Recently many people are messing around with agentic workflow, tuning prompts for a long time but still easily going off track. Actually the problem is often at the runtime layer: no budget for loops, tool permissions too broad, compression loses state. DenisSergeevitch's agents-best-practices sk…
Summary
Discusses common runtime issues in agentic workflow (loop budget, tool permissions, state loss due to compression), recommends DenisSergeevitch's agents-best-practices resource, provides a provider-neutral reference, emphasizes making permissions, budget, and observability explicit mechanisms.
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@yanhua1010: The most comprehensive introduction I've seen so far about 'Agentic Engineering Workflow'. Spent an hour reading through it completely — it could easily be turned into a paid tutorial. It covers tmux, agent memory, skills, voice input, long task execution, parallel worktree management…
Recommends a comprehensive introduction to 'Agentic Engineering Workflow', covering tmux, agent memory, skills, voice input, long task execution, parallel worktree management, multi-agent scheduling, along with the visual HTML editor Lavish and a code change validation pipeline: no-mistakes.
@knoYee_: https://x.com/knoYee_/status/2062780637677752366
The author reviews three months of experience using multi-agent collaboration, summarizing five main pain points (such as conflicts between agents, ignoring boundary conditions, self-censorship failure, difficulty in merging decisions, and exposing harder problems after compressed execution) and two insights (the high value of read-only review agents, and that agent conflicts expose ambiguous requirements), emphasizing the core decision-making role of humans in AI collaboration.
@chasen_liao: https://x.com/chasen_liao/status/2077219202608545835
This article explores the trend of upgrading prompt engineering to Agent engineering, emphasizing structured context management of AI agents through methods like AGENTS.md, and shares a minimal closed-loop workflow methodology.
@wsl8297: When running complex tasks with AI agents, the most painful thing is often not that the model isn't strong enough, but that as the conversation gets longer, the context starts to overflow. You have to keep filling in background details, re-explaining the process, plus the redundant logs from tool calls — tokens just gush out like a broken pipe. Recently, I saw TencentDB Agent Memory open-sourced by Tencent...
Tencent has open-sourced TencentDB Agent Memory, which solves the AI agent long-context overflow problem through hierarchical memory management (symbolic short-term memory + hierarchical long-term memory). Benchmarks show token consumption reduced by up to 61% and task success rate improved by over 50%.
@freeman1266: https://x.com/freeman1266/status/2055293363893768463
This article summarizes four common pitfalls encountered when deploying AI Agents from demo to production: unreliable function calling, cumulative failure rate of multi-step tasks, improper memory management, and security permission issues, along with corresponding solutions.