Lessons Learned From Building Multi Agent Workflows

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Apr 16 2026 Lessons learned from building multi-agent workflows Sarah Chieng Sherif Cherfa

I pay my upfront subscription ( $200/month ), write what I hope is the right prompt ( prompt AND context engineer ), and wait. 35 minutes later, it’s still 'synthesizing', 'perusing', 'effecting', and 'germinating' ( who came up with these) . By the end, I have files of bad code, a bloated context window, and I’m counting the remaining tokens on my left hand. Okay, I grab an apple, compact, type some heavy handed verbal abuse , re-explain everything from scratch, and pray the next attempt gets further than the last one…. only to be disappointed by the same result. By now, the spark and joys of AI coding are long dead. Stop being a one-shot Sloperator This is the single-agent ceiling . Every developer building with AI agents hits it the moment their project graduates from a 3D HTML snake game to anything more practical. This happens for reasons: We expect too much from a single agent We do not break problems into simple enough, verifiable tasks

And while this is when most people will sell you (a) a useless course on prompt engineering, (b) another SaaS tool that manages your context, (c) or ask why you haven't tried out the new model that came out seconds ago, we won't be doing that today. Instead, we're going to walk you through what actually works: running a proper back of house . Multi-agent workflows. Welcome to the back of house There are a few reasons why multi-agent workflows have become much more practical in recent weeks: underlying models have gotten better, and popular AI coding agents have made multi-agent orchestration easier to set up. In the last quarter, OpenAI rolled out deeper orchestration in Codex workflows, while Anthropic continued expanding Claude Code and the MCP ecosystem. The biggest unlock, though, is speed. Codex Spark runs at roughly 1,200 tokens/second , which makes it practical to introduce parallel and verification steps that would otherwise be too time-costly to run. For an example task using Codex and the Figma MCP to copy a website into Figma, the single agent workflow had a 36.5 min/run average with an average of 12 interventions (and 100% failure rate) while the multi-agent workflow had a 5.2 minute run, 2 manual interventions, and success on the first try. What is a multi-agent workflow? Multi-agent workflows fix the single-agent ceiling at the architecture level. Instead of one cook doing everything, you have a head chef who takes the order, breaks it into scoped, verifiable tickets, and hands each one to a line cook to execute. The Head Chef (Orchestrator) The Head Chef's job is to take the order from the human, break it into a working list of tickets, then call line cooks to each go out and complete one smaller, scoped job. The Line Cooks (Subagents) The Line Cook's job is to take the ticket (task assignment) given by the Head Chef and get the job done, no questions asked. Each line cook gets its own fresh station (context window), does its work, returns the plate, and clocks out. The trick to keeping things orderly: the line cook doesn't get the full order history. It also doesn't get your 15,000-token master plan document, it doesn't need to see all that. It gets the minimum viable context to cook one specific dish. In AI agents like Codex, you create a line cook by literally telling your agent to "use subagents." The new instance gets a prompt, a set of files it can access, and any context it needs. Three immediate wins from running a back of house There are three clear wins you get from Orchestrators and Subagents, instead of trying to one-shot whatever you are build or sticking to a single, frontier, expensive model. 1. Tokens: your effective context window goes from ~200K to 25M+ Here's how the human, orchestrator, and sub-agents interact: The human talks exclusively to the orchestrator The orchestrator is stripped of all tools other than delegate_task If the orchestrator wants to take an action, it spawns a sub-agent via delegate_task Each sub-agent has its own fresh context window, starting only with a prompt Sub-agent can read, write, use MCPs, and any other tools Sub-agent return a summary of their work back to the Head Chef

This means the orchestrator never has to read files, write files, or see tool-call results directly, effectively extending its context window to as many sub-agents as it can spawn. You can work all day without losing context, compacting, or starting over. 2. Control: you can enforce sequential workflows at each turn of the agentic loop Instead of one agent doing the exploration, cooking, tasting, and plating, each step becomes a precise, sequential ticket. This is also a great place to use different models for different tasks. With significantly faster models like Codex Spark (~1,200 toks/sec), we can add validation and QA steps that would normally be too time-costly. The orchestrator follows a script, spawning one sub-agent per phase: Sub-agent A breaks the order into a "contract" with subtasks and criteria Sub-agent B explores the next subtask Sub-agent C tests the code generated in the prior subtask a. if tests pass the validation criteria, move on b. otherwise respawn the coding line cook to fix identified issues Sub-agent D documents the subtask and updates the scope checklist If any subtasks remain, continue from step 2. Otherwise, service is done.

In internal trials, this sequential loop reduced manual interventions by 84.3% compared to single-agent runs on the same brief. 3. Speed: you can run well-defined tasks in parallel If your task permits it, you can spawn multiple sub-agents in parallel. This works well for: Generating logos, images, mascots, assets, mockups, designs, or tests Exploring a massive codebase orders of magnitude faster Building multiple pages quickly, each subagent works on separate parts of a codebase and don't overwrite each other

Running five parallel mascot generations took roughly one minute versus five minutes sequentially, about a 5x speedup on taste-driven exploration tasks. 5 Patterns That Actually Work You've just been promoted to head chef of a professional kitchen. You've got a full brigade, a board full of tickets, and no idea where to start. Over the past few weeks, we've tried dozens of...