标签：#planning

Originally published on tamiz.pro . The field of AI agents has moved rapidly from single-model executors to complex multi-agent orchestration. But after running 157 agent deployments across diverse task domains, one pattern emerged with striking consistency: planning quality predicts success far better than execution speed or model size. This isn't just theoretical—it's a practical lesson that's reshaping how engineers architect agent fleets, giving rise to what we're now calling Orca-style agents : hierarchical, planning-first systems that separate the expensive business of thinking from the cheaper business of doing. The Experiment: 157 Agent Runs Over six months, our team deployed and monitored 157 distinct agent runs across four primary use cases: code generation pipelines, automated testing workflows, infrastructure-as-code provisioning, and data transformation tasks. Each run varied along three dimensions: Architecture : Single-agent vs. flat multi-agent vs. hierarchical (Orca-style) Planning depth : No planning, brief intent statement, or full recursive planning loop Execution model : Direct LLM call per action vs. tool-augmented execution with validation The results were unambiguous. Systems that invested 3-5x more tokens in planning achieved 4.2x higher task completion rates and 3.8x fewer rollback cycles compared to agents optimized purely for fast execution. The correlation between planning sophistication and success held across every domain. Why Planning Beats Raw Execution The intuition behind this finding rests on an economic principle of LLM usage: planning is cheap relative to costly mistakes . A well-structured plan reduces the probability of executing the wrong sequence of tools, making incorrect API calls, or generating code that fails integration testing. Consider the token economics: Phase Tokens (typical) Cost impact Planning (intent + decomposition) 800–2,500 Low Execution per subtask 300–1,200 Medium Correction after failure 1,500–4,000 High

OVHcloud will raise prices from September, with 2026-edition gaming servers up 87 percent and other recent servers 40 to 59 percent. Founder Octave Klaba says memory cost six times more in June than a year earlier, as RAM suppliers shifted capacity toward high-bandwidth memory for AI. AWS, buying years ahead, has repriced one reserved GPU product. By Steef-Jan Wiggers

Have you noticed how every demo of "an AI agent" looks impressive in the video and falls apart the moment you ask a sharper question? The agent confidently does the wrong thing. It forgets what it just decided. It tries to call a tool that doesn't exist. It loops forever rewriting the same file. It calmly tells you the deployment succeeded when it didn't. These aren't failures of the model. They're failures of the workflow around the model. Because that's all an agent really is: a software workflow where a language model can pick the next step and call tools. The "intelligence" sits in the prompt and the orchestration around it, not in some secret agent-flavoured fairy dust. Strip the word "agent" away and you've got five pieces of plumbing: planning, tool use, memory, constraints, verification. Every production-grade agent stands or falls on those five. This is a long walk through each one. Not the marketing version. The kind of detail you actually need before you ship something that talks to your database. The Loop You're Actually Building Before we touch any pillar individually, hold the whole loop in your head. A useful agent does roughly this on every turn: Read the goal (and whatever memory is relevant to it). Decide the next action: answer directly, call a tool, ask a clarifying question, or stop. If it called a tool, observe the tool's result and feed it back in. Update memory if anything is worth remembering. Check constraints: are we over budget, out of iterations, touching something off-limits? Verify the output before declaring success. Loop until done or stopped. That's it. Every framework (LangGraph, OpenAI Agents SDK, Claude Agent SDK, smolagents, whatever ships next month) is a different shape of the same loop with different defaults. agent-loop.ts async function runAgent ( goal : string , ctx : AgentContext ) { const state = ctx . startState ( goal ); for ( let step = 0 ; step < ctx . maxSteps ; step ++ ) { const decision = await ctx . model . decid

Cities are dynamic, not static grids, and urbanization is a "spiky," cyclical, and asynchronous process.