Updated July 9, 2026 · Refreshed weekly
Find Users for Your Dev Tool on Reddit
Real developers asking for AI coding tools and workflow fixes on Reddit right now, updated weekly.
Developers don't discover tools from ads - they ask other developers. Subreddits like r/ClaudeAI, r/cursor, r/ChatGPTCoding and r/ExperiencedDevs see daily posts from people hitting real limits: agents losing context, vibe-coded apps that need security review, deploy bottlenecks, token costs, coordinating multiple coding agents.
Every post below was found by Leadverse and filtered for buying intent - meaning the author described a concrete workflow problem an AI dev tool can solve, not just general AI chatter. If you're building for developers, this is your market speaking in its own words, refreshed weekly.
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our token graph from last week is kind of insane (5.6B+ tokens with team of 3)
last week was... expensive 😅 we were deep in a sprint and ended up burning through almost 1B+ tokens every single day. and that's just Codex. Claude isn't even included in this graph. one thing i've learned from building with agentic workflows: the cost isn't in making one model smarter. it's in running dozens of agents in parallel, retrying, reviewing, planning, and validating until the work is actually done. AI engineering is starting to look less like autocomplete and more like operating a distributed compute cluster. how many tokens is your team burning during intense shipping weeks??11
OpenWhat setup for an agent harness
Hey there I’m trying to find the right setup to be an interface and agent harness for local development. I’ve tried a few things where I host models on LM Studio. I have two apple silicon macs with 64gb ram each and a PC with a 5080. I can utilize Claude code with a harness like Archon to distribute work but the issue is that archon doesn’t pass context or tool calls so it’s making my local models blind and somewhat stupid. In the end I spend a lot of tokens just making the local models viable. I tired loading them BYOK into copilot which functions much more effectively…. To a point. The problems are that copilot shoves anywhere from 40-60k tokens into every prompt as tool calls even on a simple “hello there!” So the 5080 can’t work at all, and the macs are almost completely filled up from word one. I got good results making qwen3.6 be the planner/architect and at first it was doing a great job coding but eventually it just gets stuck in endless tool calls loops. It can’t edit code bases without corrupting them. And it spawns dozens of agents that all overwrite each other. It’s been suggested that copilot is a little too heavy duty with prompt injection for local models so I ask: what do you all use? Opencode? Hermes? Cursor? I would love to know.32
OpenChe modello AI locale mi consigliate per il mio pc?
Salve a tutti, sono possessore di un Lenovo Legion Pro 5 i16...alcune specifiche che ricordo: \-HD- 1TB \-Ram- 32 Gb \-scheda video: rtx 5070 8GB VRAM \-processore: intel core ultra 7 So che con 8 gb di vram non posso ambire a chissà cosa, ma mi piacerebbe avere un modello in locale specializzato nella programmazione di progetti (saas) anche abbastanzi grandi e siti web. Mi piacerebbe che avesse anche delle buone skills sulla generazione testi e sul ragionamento. Solitamente utilizzo claude e con la generazione/lettura di documenti di contesto, riesco a fare abbastanza task senza consumare troppi token e vorrei che questo modello in locale riesca a farr altrettanto. Non mi interessa generazione di video, immagini e robe varie, ma solo ottima programmazione e comprensione di progetti/documenti. Che ne pensate?09
OpenAdditional burden of hosing AI apps
With AI, business and product teams are creating apps left and right. They dont understand what the code is doing, no clue about security or how to host it. This burden falls on DevOps/Engineering to now maintain it, fix it. Authors are still considered the owners of these apps. I wanted to know how are you guys handling this situation? \- Is Engineering/DevOps the defacto owners of such apps in your company? \- How are you deploying these - in your prod AWS or some hosted env? TIA919
OpenMy daily n8n + LLM prospecting workflow works, but token costs are killing me. Better architecture?
Setup: n8n cron triggers every day at 8 AM → calls an LLM agent (Hermes via OpenRouter) → agent finds 10 potential B2B clients, and for each one pulls their email, phone number, what the company does, and a short note on which of their processes we could automate → sends structured data back to n8n → n8n writes it to Google Sheets. Functionally it's fine. The problem is cost. Even on a cheap model the agent burns a ridiculous amount of tokens per run, because it's searching the web and reading full pages for every single company. 10 leads/day shouldn't cost this much. Three options I'm considering, would love input: 1. Optimize the agent — trim tool outputs, cache, stricter prompts. Has anyone actually gotten agentic research workflows down to reasonable token usage this way, or is it lipstick on a pig? 2. Rebuild in pure n8n — scrape a data source directly (business registry, Google Maps, directories?), get company name + contact info without any LLM, then use ONE small LLM call per company just to write the "what could we automate for them" summary. Cheaper, but I'm not sure what the best source to scrape is for reliable emails/phones. 3. Perplexity Sonar API — I heard it is really good for web scraping, but is the data (especially contact info) accurate enough? And what would be the price for running perplexity for this every day?46
OpenMy daily n8n + hermes agent prospecting workflow works, but token costs are killing me. Better architecture?
Setup: n8n cron triggers every day at 8 AM → calls an LLM agent (Hermes via OpenRouter) → agent finds 10 potential B2B clients, and for each one pulls their email, phone number, what the company does, and a short note on which of their processes we could automate → sends structured data back to n8n → n8n writes it to Google Sheets. Functionally it's fine. The problem is cost. Even on a cheap model the agent burns a ridiculous amount of tokens per run, because it's searching the web and reading full pages for every single company. 10 leads/day shouldn't cost this much. Three options I'm considering, would love input: 1. Optimize the agent — trim tool outputs, cache, stricter prompts. Has anyone actually gotten agentic research workflows down to reasonable token usage this way, or is it lipstick on a pig? 2. Rebuild in pure n8n — scrape a data source directly (business registry, Google Maps, directories?), get company name + contact info without any LLM, then use ONE small LLM call per company just to write the "what could we automate for them" summary. Cheaper, but I'm not sure what the best source to scrape is for reliable emails/phones. 3. Perplexity Sonar API — I heard it is really good for web scraping but is the data (especially contact info) accurate enough? And what would be the price fo it to run every day to find costumers?1514
OpenWhere are you storing AI agent session data? Session logs, tool calls, file diffs
Running coding agents that generate a lot of artifacts per session: - Conversation history / reasoning traces - Tool call logs (file reads, shell commands, search results) - File diffs / patches the agent produced - Checkpoints (so you can resume or rollback) - Token usage / cost tracking Right now I'm just dumping JSON files to disk but it's getting unwieldy. Curious what others do: 1. Flat files (JSON/JSONL per session)? 2. SQLite/Git repo per project? - Do you keep raw token-level logs or just summaries? - Anyone doing replay/debug from session logs? - Do you version session state so the agent can resume mid-task? My use case: want agents to pick up where they left off, and want to audit what they did. But don't want to build a whole observability platform just for this.37
OpenVibe-Coded Apps
Hi community.. QQ, as a group of CISO’s are you accepting of business folk vibe-coding apps that solve specific business problems? And if yes, how do those apps then get deployed into your infra (so they can talk to your internal systems)? If no, why? And are you going to block these types of apps somehow? How would you stop them deploying outside your firewall and simply exporting data and loading it into the external app? Neil24
OpenVibe-Coded Apps
Hi community.. QQ, as a group of CIO’s are you accepting of business folk vibe-coding apps that solve specific business problems? And if yes, how do those apps then get deployed into your infra (so they can talk to your internal systems)? If no, why? And are you going to block these types of apps somehow? How would you stop them deploying outside your firewall and simply exporting data and loading it into the external app? Neil016
OpenIs anyone switching to Codex for Sol 5.6?
Hey everyone, I've been on the Claude Max20 plan for 8 months, but the token limits are driving me crazy lately. Between the recent Fable 5 updates, the temporary extensions, and the insane token burn with Fable 5 and Opus 4.8, I'm constantly hitting the wall. I'm locked out again and tired of waiting. I'm seriously considering canceling Max20, dropping Claude Code, and switching to Codex for Sol 5.6. Before I jump ship, I have a few questions for anyone using it: How good is Sol 5.6 with complex, large repositories and multi-file edits compared to Claude? What about the token burn on Codex? I heard Sol Ultra uses cooperative subagents that burn through limits 6x to 12x faster. Are the restrictions any better than Claude's? Is Codex with Sol 5.6 a viable terminal replacement for Claude Code in production? I love Claude's reasoning, but these limits are killing my productivity. Is anyone else making the move, or am I just jumping into another token-burning furnace? Thanks for any insights!019
OpenHow do you handle prompt reviews when the domain expert isn’t technical?
I am planning to build agents for a few workflows in finance, insurance and internal support, and I keep running into the same bottleneck: The person who actually knows whether a prompt is correct (underwriter, the support lead) has no interest in touching the code, but I’ m the only one who can push a change to production. What I have already tried so far: 1/ Keeping prompts in the codebase and having them send edits over Slack for me to paste it in. Fast at first, turns into a real challenge the moment there’s more than a single prompt to manage, and there’s no real audit trail of who changed what exactly. 2/ Moving prompts into a shared document so they can edit freely. Better for them, but now the doc and the deployed version drifts apart constantly, and I’m manually reconciling both. 3/ Giving them up with limited repo access. Sounds clean in theory, but branching workflows are hard sell for the ones who has never touched git. None of these felt sustainable once we needed actual sign-off on changes, not just someone edited it somewhere. Spent a lot of time on a few of these AI ops/observability platforms that sit near to the workflow layer instead of raw git. The one which came across OrqAI in that search, seems to let non-technical folks edit and version prompts through a UI while still keeping everything traceable, but I haven’t run it in a regulated setup yet, so I can’t vouch for how it holds up under real compliance scrutiny. Can someone tell me what’s actually working for people here, especially for the ones who are shipping into anything where someone non-technical has to formally approve every prompt change before it goes live.21
OpenWhich open source/open weight local LLM should I try for coding purposes.
I am a researcher and have been using claude for the past 2 years, but now open source/weights models are also performing good. And due to Claude limits I want to have a locally hosted model for my coding purposes. I do serious coding work and algorithm design. So for my work which open source model will be good. Recently i have heard about GLM-5.2, kimi k-2.6, Mimo, etc. Which models i can use for my task. And also which agentic harnesses like opencode, PI, hermes, claude code, etc. these perform well. I have 4x Nvidia Pro 6000 Blackwell server edition, and 8x H100. I want to know the best model out of these open source because my work is confidential and i want my team to use a locally hosted model rather than using gpt or claude611
OpenHow are you handling agent loop errors? Thoughts on a middleman proxy approach?
Hey everyone, I’ve been experimenting a lot with OpenClaw recently for multi-tool tasks, and as awesome as it is, the "Token Bleed" from agent loops is getting expensive. I watch my agent get stuck, like calling the same tool 30 times. Right now, the standard fix is to set step limits or hard-abort. But that just kills the run. I’m curious about a different, higher-level approach and wanted to get your thoughts: What if we solved this using a smart "middleman" API proxy instead of coding fixes inside our agent app? Here is what the idea aims to do: A Safety Net: The proxy sits between your agent and the LLM API, watching the conversation. Spotting the Loop: It automatically recognizes when the agent is repeating itself, getting confused, or running into a wall. Active Real-Time Healing: Instead of crashing or stopping the agent, the proxy quietly injects a quick "how-to-fix" tip directly into the conversation stream in-flight. The LLM reads it, corrects its course on the next step, and keeps going. A Collective Memory: Since it's a shared proxy, once it learns a sterile, anonymous way to solve a specific loop (like a Python environment error) for one user, it can instantly use that lesson to rescue another developer's agent when they hit the same wall. The main goal is to keep the agent alive and rescue the run without bloating your local agent code with endless custom error-handling logic. Does an API-level middleman approach make sense for this? What are the biggest drawbacks you see ?10
Openhermes credit usage
has anyone else had issues with hermes burning through credit on a single small task. I askeed it to open a browser over whatsapp just to test the computer usage skill was working and it burnt through $5 in a minute. I am fairly sure it was on sonnet but it may have been opus. But even so I don't unnderstand how it did that in 1 minute.520
OpenWhat are the top platforms you have used to prevent AI agent sprawl?
We’re starting to see AI agent sprawl in a very real way, different teams are spinning up their own agents on whatever stack they prefer, pointing them at internal APIs and SaaS tools, often with broad credentials and limited oversight. I’m trying to find platforms that actually help prevent AI agent sprawl rather than just giving another dashboard. I’m especially interested in tools that can act as an AI agent registry or agent governance layer, where you can see all agents in one place, assign a clear owner, define scoped access for each agent, enforce a central entry point or policy layer for agent traffic, and record agent activity in a way that is easy to search when something goes wrong. If you’ve already been through this, which platform or combination of platforms did you choose to control AI agent sprawl, what specific problem did they solve for you, and did anything that looked promising turn out to be a poor fit once you tried to use it at scale?44
OpenWhen it comes to the weekly usage are two 5x plans better than 1 20x plan?
I run out of usage 4 days into the week with 5x so need to upgrade.122
OpenI think Prompt Engineering is becoming Context Engineering.
Over the past few weeks, I've noticed a shift. Instead of talking about better prompts, more AI companies are talking about better context—how you manage memory, tools, state, retrieval, and constraints around an LLM. I've been reading through a bunch of documentation, and these five resources completely changed how I think about building AI agents. 1. LangChain — Context Engineering Probably the best introduction to Context Engineering. One line really stuck with me: 2. Anthropic — Model Context Protocol (MCP) If AI agents are going to interact with tools and external systems, MCP feels like one of the most important standards to understand. It's becoming the common language between LLMs and tools. 3. OpenAI Agents SDK A great way to understand how modern agents are actually built: tools memory handoffs guardrails sessions It helped me realize an "agent" is much more than just a good prompt. 4. Google Agent Development Kit (ADK) Really interesting if you're curious how Google approaches multi-agent systems, sessions, workflows, and tool orchestration. Worth reading even if you don't plan to use Gemini. 5. Microsoft Agent Framework Microsoft's framework puts a lot of emphasis on workflows, memory, checkpointing, human-in-the-loop, and MCP integration. It's a good reference if you're interested in production-ready agent architectures. A year ago I spent most of my time trying to write better prompts. Now I'm spending much more time thinking about: What context should the model receive? Which tools should it have access to? What should it remember? What should it forget? How should state flow between steps? That feels like a much bigger lever than prompt wording. Curious what everyone else is reading. Any docs, papers, GitHub repos, or blog posts on Context Engineering that you'd recommend?10
OpenHow to secure AI applications in production environments: best practices and tools
been trying to figure out the right way to secure ai apps once they leave the lab and it feels like every vendor has a different answer. we're seeing more internal use cases with llms and ai agents, some customer-facing too, and the part i'm stuck on is how people handle the boring but important parts in prod. access control, full inference trace logging (prompts, retrieved context, tool call args, model responses), data leakage, secrets management, model abuse, rate limiting, runtime policy enforcement, and whatever else i'm probably missing. i keep seeing pre-deployment tooling (llm evals, ai red teaming) sitting next to runtime tooling (prompt guardrails, llm observability), but it's hard to tell what holds up vs what just looks good in a demo. same with arch, some people put everything behind an api gw and call it a day, others run full policy layers and separate services for every piece of the stack. what are you running in real envs? interested in what held up, what turned into noise, and any tools or patterns you'd avoid if you're trying to ship a secure llm app without making a mess later.69
OpenJust rejoined Cursor after about a year a way. Am I doing something wrong?
Hi. Decided I would use an "affordable" model to make my limits last - opted for GLM 5.2 (high). I've only sent 4 prompts (different chat every time) and I've already used 9% of my monthly quota... this is with the CHEAPER model? Am I doing something wrong? Don't get me wrong, Composer 2.5 seems great, and I'm using it for the majority of my code, but for stubborn problems that Composer can't fix, I've switched to GLM 4 times, and I'm scared to use it again. I can't even imagine how much of my limit I'd use up if I actually used a model like GPT-5.5 or Claude. I'm also using GrapeRoot MCP to try and lower token usage. What am I doing wrong?1820
OpenWhat's codex estimate usage for gpt-5.5 vs gpt-5.4?
Hey guys I'm using codex but mainly using gpt-5.4 because I think it's saving me some tokens. But I tried to search around and found no answer of it actually matters. So does using gpt-5.4 makes the tokens or plan last more than with gpt-5.5? Also which model are you using with hermes. I can only see gpt-5.5. gpt-5.4. And gpt-5.4 mini. Some people saying 5.3 codex or something but I can't even pick in my model selection. So what's your model of choice guys? Also should I keep using codex or use z.ai glm5.2 instead? If someone had tried them both please tell me if it's a good idea to switch to glm plan or not.110
OpenComing from Codex, it seems like Claude is just burning through tokens doing nothing. How to control it better?
I just started using Claude recently after a year of Codex, and I'm just amazed at how it manages to just burn through your tokens for the simplest tasks with no feedback whatsoever. For instance, I'll give it a simple targeted prompt in a very small codebase, and it will just burn more than 15k tokens thinking for minutes to only generate a 5 lines diff. In comparison, Codex just did it immediately and barely scratched my daily usage. Now I can only use it for a few prompts before busting the 5h window (whether it be Opus 4.8 or Fable 5 on high) and it just feels completely unproductive. I must be missing something no? I'm using the official vscode extension btw.919
OpenBig agent sims
Anyone running a high volume of agent tests using long-form sessions? What kind of run sizes would be optimal, and what kind of feedback loops (other than the obvious- tool call failure, memory formation) are optimal? I don't see a lot of literature on this. Thanks!13
Openwhat are you all using for a local agent that can do a bit of everything?
so i've been going down the local AI rabbit hole and want to build something fully self hosted on my proxmox setup. no cloud APIs, all running on my own hardware. basically i want an agent (or honestly whatever combo of tools works) that can do three things: research stuff (web search, read docs, summarize, maybe rag over my own files) actually help with coding, like real edits not just chatting about code run terminal commands / help me with homelab stuff without me babysitting every step General use just chatting and memory saving. Ive seen things with openclaw and hermes, not sure what ppl reccomend for this kind of stuff. i can't tell if the move is one platform that does it all or just running ollama with like 2-3 different frontends for different jobs. so for anyone actually running this daily: what's your actual setup? one tool or a stack? which local models have been solid for tool calling? been seeing qwen coder recommended a lot how do you do web research without cloud stuff? searxng? browser? how do you give it terminal access without it nuking your system lol. docker sandbox? read only first? proxmox: lxc vs vm vs just running ollama on the host?718
OpenHas anyone noticed any strange behavior with the limits?
For at least a week now, I’ve had the feeling that the limits have been drastically cut. I have a 20x plan and before, I could easily run 3–4 sessions in parallel and actively work on all of them without hitting the limits Now my limits only last for about 10–15 prompts per session, and these are just basic edits. I actively monitor the context, constantly use clear and compact, and don’t use Fable at all, but my limits are running out faster than ever1811
OpenStrategies for working interchangeably with other models
Since Claude Code has just cut me off for the first time ever using the Claude Max Plan, I am looking for strategies others have used switch providers while being put into time out until 5:50 PM (literally treating us like toddlers). The issue is maintaining the knowledge base/memory of the codebase with each provider. I understand about sending handoff.md files and ensuring initial onboarding is done with the secondary model. One other comment I would make: Anthropic unintentionally just created the justification for me to start using competing models. I anticipate that with this week's announcements of Gemeni 3.5 Pro along with Codex 5.6, Claude Code will have some actual real competition (desperately needed). I suspect by July 15th, churn will be a real issue for them. Anthropic continues to make decisions that only further alienate its most important customers10
OpenNeed some help with saving tokens on Fable via sub-agents - does Sonnet 5 make any sense?
Greetings. Dunno whether anybody else realized too that making subagents on Sonnet doesn't work anymore in terms of saving tokens. Previously, I used Sonnet-4.6 subagents, and they were great, cheap, and did the job. Now we are forced to Sonnet 5 subagents which are about as expensive in delivering the result as Opus 4.8. I explicitly told Fable I need to outsource the tasks to Sonnet-4.6. He agreed. Then I looked into .jsonl of the project. And there was Sonnet-5. I am on Max-5x plan ($100), it took me 1 hour on Fable to spend my 5-hour limit, including 10% of my weekly limit - even though I told Fable only to plan and review and orchestrate on its own, but use Sonnet-4.6 for coding. It simply didn't change anything. What is the purpose of using Sonnet 5 at all? Is there any way to orchestrate a pipeline that would be token-efficient like before? I don't have OpenAI subscription, I can't make it use GPT-5.5 for coding. But like now it just doesn't save anything. Yeah, Fable 5 is fantastic. But can we have some token-efficient reasonable models that make sense as sub-agents, please? (It's not about Fable performance so I guess this post doesn't have to go into the megathread)53
OpenHow do you balance frontier and mini agents to get most of the performance for less money?
I have been playing with GPT5.4 and GPT5.4-mini. The former is 3 times more expensive than the latter, so I am trying to get most of the job done by mini to save on the bill. Everyone says to do thinking with a frontier model, and the heavy lifting with a smaller model. I have been trying to do that with this AGENTS.md, defining an "orchestrator" and a "coder": Delegation policy (cost control) You (the root session, gpt-5.4) are the orchestrator. You MUST NOT write or modify implementation code yourself except trivial one-line glue. For any task that involves writing, modifying, or refactoring code — however small it seems — spawn the "coder" subagent (defined in .codex/agents/coder.toml, gpt-5.4-mini) and wait for its result. Your own role is limited to: planning, breaking phases into tasks, providing the coder with full context (relevant sections of AGENTS.md / ARCHITECTURE.md / ROADMAP.md / TOOLING.md), verifying the coder's output against the phase's Definition of Done, and updating ROADMAP.md / DECISIONS.md / OPENQUESTIONS.md. If you catch yourself about to write a code block or a patch directly instead of spawning "coder", stop and spawn "coder" instead. It failed miserably, as I used about the same amount of GPT-5.4 tokens as of GPT-5.4-mini tokens, as the orchestrator spends a lot of time reading the code, giving directions, and so on. I am going to try something else with a new "architect" persona, something like: Delegation policy (cost control) You (the root session) are the orchestrator (gpt-5.4-mini). Your job is planning, dispatching, and verification, not implementation or deep reasoning. Roles - "coder" (gpt-5.1, defined in \.codex/agents/coder.toml\): default destination for ALL implementation work — writing, modifying, or refactoring code, however small it seems. You (the orchestrator) MUST NOT write or modify implementation code yourself, except trivial one-line glue. If you catch yourself about to write a code block or a patch directly instead of spawning "coder", stop and spawn "coder" instead. - "architect" (gpt-5.4, defined in \.codex/agents/architect.toml\, read-only): reserved for a small number of high-value moments only — never for routine dispatch: - verifying a phase's output against its Definition of Done before marking it \done\ in ROADMAP.md; - resolving a design ambiguity not already settled in ARCHITECTURE.md/DECISIONS.md; - an explicit request from the user. Do not spawn "architect" for status checks, routine dispatch, or anything "coder" can reasonably decide on its own with the existing docs. But it feels like I am reinventing the wheel. I mean everyone on Earth must have this issue, and I'm sure there a correct way to do that, I just have not been able to find it. Thank you for your constructive criticism!114
OpenWhy are token costs so inconsistent?
I tried comparing our AI token usage with the bill we got and usage looks steady week to week but costs were not consisntent at all even though nothing obvious changed Last month we had the same flows running at the same volume but a small tweak we made to a prompt moved the numbers more than expected and even some of the spend isn’t sitting in the same place as usage so it’s hard to trace what’s causing it until the final bill comes.2015
OpenVibe Coding a web app, but hitting a ceiling. How do you manage context and polish UI details?
Hey everyone, I’ve been heavily experimenting with the vibe coding workflow lately to build some web apps. While it’s incredibly empowering to just talk to an AI and watch features appear, I’m noticing a massive variance in output quality, and honestly, I feel like I’ve hit a bit of a ceiling. My biggest issue right now is what I call the "hollow idea" syndrome. I’ll start with a vague concept, like wanting to build a sleek budgeting app, but because I don’t have a rigid PRD or a design background, the initial output always feels incredibly generic. I really struggle with how to guide the AI to iterate deeply on features and UI design without it just giving me basic boilerplate. On top of that, once the project starts scaling, the vibe quickly turns into a firefighting session. The context gets bloated, the AI starts hallucinating, and it constantly introduces regression bugs where fixing a feature on page A completely breaks something on page B. I’d love to know how fellow builders are handling this and actually shipping high-quality, polished web projects. How do you guys flesh out a loose idea before you even start prompting? Do you force the AI to write a full spec sheet first, or do you just wing it and pivot on the fly? Also, I'm really curious about your exact workflows and stacks—like how you transition from UI generators to your main IDE, how you manage context to avoid code rot, and how you prompt for that final 20% of premium polish so the UI doesn't look obviously AI-generated. If you have any system prompts, rules, or even horror stories that taught you a lesson, please drop them below. Thanks!34
OpenHow do teams stop AI coding agents from making conflicting assumptions?
Recently I have worked in some teams where most of the engineers are using a coding agent and sometimes multiple at the same time. I was wondering if anyone had similar issues around coding agents working together over a larger project. How do you prevent each person’s agent from making different assumptions about APIs, architecture, naming, existing patterns, or requirements? For example, one agent changes an API shape while another builds UI/tests/docs against a different assumption. \- Do tickets/specs solve this? \- Do ADRs or architecture docs help? \- Do agents actually read/use them? \- Do conflicts usually show up during planning, during implementation, or only in PR review? \- What process have you added specifically because of AI-generated code?19
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