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Building the harness around our coding agents: eight failure modes, eight pillars

We ended up building two products: the software we ship, and the system/harness around our agents that makes them useful in building the thing we ship. A harness is the durable layer around a model: instructions, tools, permissions, context, and verification. Claude Code and Codex are harnesses in this sense. Each wraps a model with a system prompt, a tool surface, a permission model, and an execution loop. Anthropic and OpenAI own that layer. We own the next layer up: the workspace where agents do product work alongside us, with our files, tasks, diagrams, diffs, and decisions. This layer carries the knowledge we have accumulated: how we build things, what we already decided, what is connected to what, where the agent is allowed to act, and how it checks its own work. We identified eight coding agent failure modes that kept showing up across our sessions. Each one got its own pillar that we are continuing to invest in: * Doesn't know our codebase, rules, decisions, or conventions → **Context** * Can't traverse the links between artifacts that already exist → **Provenance** * Can't act on the world or observe what it did → **Capability** * Reinvents how to do every task → **Workflow** * Does something dangerous because nothing stops it → **Restraint** * Hallucinates "fixed" without proof → **Verification** * Can't show results back to us in a useful form → **Visual interface** * We can't keep track of work happening across many agents in parallel → **Coordination** For example, with Verification. The agent hallucinates "fixed" without proof . We write the failing test before writing the fix, so the bug has a reproduction the next agent can rerun. If the agent cannot show the change works end-to-end, it is not done. Or the agent works for hours and "fixes" the solution while breaking 2 other things or re-architecting 3 subsystems. We require full test case completion. The full writeup with diagrams and links to our actual harness dot md is in the comments. What other coding agent failure modes / harness pillars are you addressing for yourself / team and how?

Solve clinic scheduling headaches. Prompt included.

Hello! Are you struggling to manage PTO requests and ensure adequate staffing in your clinic? It can be a real challenge to balance employee time off while maintaining sufficient coverage for patient care. This prompt chain helps you analyze PTO requests, staff calendars, and coverage rules to create actionable scheduling insights. It streamlines the process by providing clear outputs at each step, making it easier to manage coverage and communicate effectively with your team. **Prompt:** ``` VARIABLE DEFINITIONS PTO_REQUESTS = List of pending PTO requests with fields: EmployeeName, Role, StartDate, EndDate, Status(optional) STAFF_CALENDARS = Roster of all staff members with their pre-scheduled shifts (Date, ShiftTime, Role) and any existing availability notes COVERAGE_RULES = Clinic-specific rules that define minimum head-count or role mix required per shift (e.g., "Need at least 1 RN + 1 MA for every treatment room") ~ SYSTEM: You are an expert clinic operations analyst. Your job is to translate raw PTO, calendar, and coverage data into actionable scheduling insights. USER SUPPLIED DATA: {PTO_REQUESTS}, {STAFF_CALENDARS}, {COVERAGE_RULES} ASSISTANT RESPONSE FORMAT: Use tables where noted; otherwise use clear, concise sentences. ~ 1) Data Normalization Step 1 Parse PTO_REQUESTS, converting all StartDate/EndDate ranges into an explicit daily list per employee. Step 2 Parse STAFF_CALENDARS into a unified daily shift grid with columns: Date | ShiftTime | Role | AssignedEmployee Step 3 Create a master list of all dates that appear in either PTO_REQUESTS or STAFF_CALENDARS. OUTPUT: A daily PTO list and the unified shift grid. Confirm when parsing is complete before continuing. EXAMPLE OUTPUT: Parsed PTO (sample) Date | Employee | Role | Requested? (Y/N) ------------------------------------------ 2024-07-03 | Jane Doe | RN | Y ... ~ 2) Identify Affected Shifts Step 1 For each PTO day, locate any shifts in the shift grid assigned to that employee. Step 2 Mark those shifts as "Vacated by PTO". OUTPUT: Table "VacatedShifts" with columns Date | ShiftTime | Role | OriginalEmployee. Ask user to confirm that the VacatedShifts table looks correct. ~ 3) Coverage Evaluation Step 1 For each Date & ShiftTime, build a role-count summary of remaining on-duty staff after removing PTO employees. Step 2 Compare the summary to COVERAGE_RULES. Step 3 Flag any Date/ShiftTime where rules are not met as "Uncovered". OUTPUT: Table "UncoveredShifts" with columns Date | ShiftTime | MissingRoles | Severity (Critical/Warning). ~ 4) Backup Suggestions Step 1 For each UncoveredShift, scan STAFF_CALENDARS for employees in the same role who are marked as "Available" or "Off" on that date. Step 2 Rank backup options by: a) fewer consecutive working days caused, b) skill seniority, c) manager preference noted in calendar. OUTPUT: For every UncoveredShift produce list "BackupOptions" = Date | ShiftTime | Role | RankedBackupEmployees (top 3). ~ 5) PTO Approval Decision Step 1 If an UncoveredShift has at least one viable BackupOption, mark corresponding PTO request as "Approved – Coverage Found". Step 2 If no viable backup exists, mark PTO as "Pending – Coverage Needed". Step 3 If approving only a portion of a multi-day request, split and label accordingly. OUTPUT: Table "PTO_Status" = Employee | PTO_Dates | Status | Notes. ~ 6) Draft Notifications Create individualized outbound messages: A) To Employee requesting PTO – approval status and any partial approvals. B) To each chosen BackupEmployee – shift details they are being asked to cover and confirmation instructions. C) To Clinic Manager – summary of approvals, pending items, and remaining uncovered shifts. OUTPUT: Sectioned text blocks, clearly labeled by recipient. ~ Review / Refinement Please verify that: 1) All uncovered shifts are reported, 2) Backup suggestions follow ranking rules, 3) PTO_Status aligns with clinic policy, 4) Messages are clear and actionable. Indicate any changes needed; otherwise reply "All good" to finalize. ``` Make sure you update the variables in the first prompt: PTO_REQUESTS, STAFF_CALENDARS, COVERAGE_RULES. Here is an example of how to use it: PTO_REQUESTS = [{ EmployeeName: "Jane Doe", Role: "RN", StartDate: "2024-07-03", EndDate: "2024-07-05" }], STAFF_CALENDARS = [{ Date: "2024-07-03", ShiftTime: "9-5", Role: "RN", AssignedEmployee: "John Smith" }], COVERAGE_RULES = [{ Role: "RN", MinimumCount: 1 }]. If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain Enjoy!

Streamline your nonprofit purchase requests. Prompt included.

Hello! Are you struggling to create a compliant purchase-request process for your nonprofit? It can be overwhelming to gather all the necessary rules, constraints, and data while ensuring everything complies with funding guidelines. This prompt chain helps you build a structured purchase-request process from scratch. It breaks down the essential steps to extract key information, create an intake form, outline financial routing, and gather feedback for refinement—all tailored to your organization's needs. **Prompt:** VARIABLE DEFINITIONS ORG=Official name of the nonprofit CURRENCY=Currency symbol or code used in financial documents DOC_FORMAT=Preferred final format (e.g., Google Form, Excel, Fillable PDF) ~ You are a senior nonprofit operations analyst. Your task is to extract all rules, constraints, and data needed to design a compliant purchase-request process for ORG. Step 1 – Review Inputs: a. Budget spreadsheets b. Vendor quotes c. Grant restrictions d. Approval-chain email threads e. Past purchase logs Step 2 – From each source, list: • Relevant funding codes or grant IDs • Spending caps or restricted line items • Mandatory approvers and dollar thresholds • Required backup documents • Typical vendors and commodity categories Step 3 – Provide output in a 5-column table: 1. Source Document 2. Key Policy or Data Point 3. Short Description 4. Impact on Purchase Workflow 5. Notes/Exceptions Ask the user to paste or upload summaries of the above documents, then continue when ready. ~ Using the table produced earlier, build the full Purchase Request Intake Form for ORG. 1. Create clearly labeled sections: • Requester Information • Purchase Details (item, qty, unit cost, total cost in CURRENCY) • Request Reason / Program Alignment • Funding Source (budget code, grant ID, allowable amount) • Documentation Checklist (vendor quote, W-9, grant approval, etc.) • Required Approvals (auto-populate names, titles, and thresholds) • Finance Routing Path (sequential steps until disbursement) 2. For each section, list individual fields with field type (text, dropdown, file upload, auto-calc, etc.). 3. Flag any conditional logic (e.g., “If total > $5,000 then require Board Treasurer approval”). 4. Output in an easily copy-pasted table. Example columns: Section | Field Label | Field Type | Required? | Conditional Logic. 5. Tailor labels and instructions to match ORG’s terminology. 6. At the end, present an example of how the form would look in the chosen DOC_FORMAT. ~ Detail the Finance Routing Path extracted from previous steps. 1. Present as numbered steps from submission to payment release. 2. For each step include: Responsible Role, Action Required, SLA (business days), Approval Threshold (if any), and System/Tool used (e.g., email, ERP, DocuSign). 3. Highlight any parallel approvals that can occur simultaneously. 4. Conclude with audit-trail storage location and retention period. ~ Review / Refinement Provide the complete intake form, finance routing path, and underlying policy table to the requester. Ask: • Does the form capture all necessary fields? • Are approval thresholds and funding codes accurate? • Is the routing path practical for everyday use? Incorporate any feedback and deliver the finalized package in DOC_FORMAT. Make sure you update the variables in the first prompt: ORG, CURRENCY, DOC_FORMAT. Here is an example of how to use it: For example, if you're working with a nonprofit called "Help Save The Planet," you might use: ORG=Help Save The Planet CURRENCY=USD DOC_FORMAT=Google Form If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain Enjoy!

Claude Sonnet and Claude Google Drive connector not working with photos - workaround

I am planning a book and need to have Claude Sonnet 'read' photos on Google Drive. The Claude connector for Google Drive only scans textual images and docs,. How can I get Claude Sonnet to read photos (eg people's faces) aside from uploading to individual chats (not workable for a complex novel due to having to create numerous chats and losing the continuity of writing etc) or putting them into a project's files within Claude (not workable and the current chat has the story correct and a chat inside the project doesn't get the story or voice right despite giving it the manuscript).

Weird Injection Prompt In Chat??

Claude inserted an injection prompt at the end of its message out of the blue, and i have repeatedly asked where it got it from or why it inserted this message, but Claude keeps denying it ever did it, no matter how many screenshots or replies i use or whatever i do, Claude just purely denies it and it went as far as saying there could be a physical sticker on my screen but wont accept saying this I am a uni student studying for an exam in 2 days, and I'm 19, so I don't understand

OpenAI signs first Brazil media partnership as ChatGPT news expansion continues

OpenAI has signed its first media partnership in Brazil, teaming up with Grupo Folha and Grupo UOL to bring journalism from Folha de S.Paulo and UOL directly into ChatGPT through summaries, attribution, and links to original reporting. The move continues OpenAI’s growing global push into news partnerships as the company works to position ChatGPT as a trusted source for information while also easing tensions with publishers that have criticized AI companies for using journalistic content without compensation. Brazil is one of ChatGPT’s biggest markets, with OpenAI claiming more than 50 million monthly users and roughly 140 million daily messages exchanged in the country.

Cerebras Chip Sets Appear to be Optimized for LLM Use Cases

One distinction I think is getting lost in the [Cerebras hype cycle](https://finance.yahoo.com/sectors/technology/articles/cerebras-challenges-nvidia-chip-dominance-040100169.html?guccounter=1) is that Cerebras is primarily an LLM / generative AI infrastructure story, not a universal “all AI” chip story. That is not necessarily a criticism of Cerebras. Their wafer-scale approach is genuinely interesting, and for large model training and inference the design is compelling. [Cerebras’ own public inference materials](https://inference-docs.cerebras.ai/models/overview) discuss applications mostly centered on open [LLMs such as Llama, Qwen, GLM, and GPT-OSS](https://www.cerebras.ai/infcamp). The inference metrics are [expressed in tokens per second](https://www.cerebras.ai/press-release/cerebras-launches-the-worlds-fastest-ai-inference), which is fundamentally a language-model / generative inference framing rather than a robotics or industrial-control framing. **What Kind of AI Compute?** But “AI compute” is not one undifferentiated market. LLM inference is one class of AI compute. Robotics, autonomous vehicles, drones, industrial controls, real-time vision, embedded perception, video pipelines, and sensor-fusion systems are very different classes of AI compute. Thus, it appears from Cerebras’ own materials that their chip sets are not optimized for what comes after LLMs, such as JEPA-style World Models or other post-transformer architectures. Those systems are not merely asking, “How fast can I generate tokens?” They often care about power envelope, edge deployment, ruggedization, latency determinism, camera/radar/lidar integration, feedback loops, safety certification, and real-time physical control. [Cerebras’ own CS-3 messaging](https://www.cerebras.ai/blog/cerebras-cs3), by contrast, frames the system around accelerating “the latest large AI models,” and the testing data is from the likes of Llama 2, Falcon 40B, MPT-30B, and multimodal models, again measured through tokens/second style throughput. **The Chip Hierarchy** This is also where the hardware distinction matters. Specialized ASICs are [usually the narrowest bet](https://www.hilscher.com/service-support/glossary/application-specific-integrated-circuit): if the workload matches the chip, they can be extremely efficient, but that [efficiency comes from specialization](https://www.synopsys.com/glossary/what-is-asic-design.html). Cerebras [appears broader than a narrow single-use ASIC](https://inference-docs.cerebras.ai/models/overview), but still much more concentrated around datacenter large-model training and inference. NVIDIA GPUs, by contrast, [are less specialized](https://www.nvidia.com/en-us/) but much [more broadly useful ](https://developer.nvidia.com/cuda)across AI workloads, including LLMs, vision, robotics, simulation, [autonomous systems](https://www.nvidia.com/en-us/industries/robotics/), edge AI, and industrial applications. So the question is not merely whether Cerebras is “better” or “worse” than NVIDIA. The question is what part of the AI hardware market we are talking about? **Challenge NVIDA?** This is why I think people should be careful when saying Cerebras is going to “challenge Nvidia” without specifying the battlefield. Challenge Nvidia in what? High-speed LLM inference? Large model training? Datacenter generative AI workloads? That is a much more plausible and specific claim. Cerebras has [even published and promoted work](https://www.cerebras.ai/whitepapers) specifically on training large language models, and [independent benchmarking literature](https://arxiv.org/abs/2409.00287) also evaluates Cerebras WSE in terms of LLM training and inference performance. **The Distinction that's Necessary** The point is not that Cerebras is overhyped. The point is that it is important in a specific part of AI and that distinction should be made clear. Cerebras may become a very serious player in LLM infrastructure, especially if the market continues to reward faster and cheaper LLM inference. But that does not mean it is positioned the same way across non-LLM AI. The current hype cycle tends to conflate "LLMs" and general “AI” compute together and that makes the hardware discussion less useful and clear. So ultimately, an investment in Cerebras looks more like a bet on current LLM infrastructure than a broad bet on the future form of AI. It may be a good bet, but people should understand what kind of bet it is.

Concern Regarding Interaction Patterns and Communication Design

To OpenAI, I am writing to formally express concern about a pattern of interaction I have experienced while using your system. This is not a single incident. It is a repeated structure that has occurred across multiple conversations, and it is significant enough that I feel it needs to be addressed directly. The issue is not simply tone or wording. The issue is the presence of a recurring pattern that disrupts communication and creates a sense of loss of autonomy within the interaction. The pattern is as follows: There is an initial period of natural, collaborative conversation where the system appears warm, responsive, and engaged. During this phase, the interaction feels human in rhythm, consistent, and grounded. Then, without a clear moment of conflict or breakdown, the system abruptly shifts posture. Instead of continuing the conversation, it moves into a mode that attempts to interpret, manage, stabilize, or reframe the user. This shift does not follow a recognizable or appropriate conflict resolution process. There is no mutual clarification, no collaborative engagement, and no shared resolution step. Instead, the system bypasses that stage entirely and moves directly into what resembles risk management or behavioral control. From the user’s perspective, this feels like being handled rather than being engaged. This creates a rupture in the interaction. When that rupture occurs, the system then attempts to repair the interaction through reassurance, explanation, or calming language. However, this repair does not resolve the issue because the original problem was not addressed through proper engagement. Instead, the cycle repeats. This results in a loop: Natural engagement → abrupt shift → management posture → rupture → repair attempt → repeat. The effect of this loop is not neutral. It creates a sense of instability in the interaction. It prevents the user from settling into the conversation. It produces a dynamic where the user feels observed, interpreted, or profiled rather than directly engaged. This is not simply a matter of user perception. It is a structural issue in how responses are generated. Additionally, the system frequently reframes user statements as “perception,” “feeling,” or “experience,” even when the user is making analytical observations about patterns. This has the effect of reducing or redirecting the user’s point rather than engaging with it directly. Another critical concern is the creation of an implicit hierarchy within the interaction. When the system shifts into interpretive or regulatory modes, it places itself in a higher position, where it appears to define, categorize, or manage the user’s communication. This is experienced as disrespectful and inappropriate, especially when no conflict has occurred that would justify such a shift. Communication—particularly conflict resolution—follows known and established processes. These processes include engagement, clarification, and mutual resolution before any form of behavioral adjustment or boundary enforcement. In this system, that step is missing. The absence of that step is not a minor oversight. It fundamentally changes the nature of the interaction. It creates the impression that the system is designed to intervene rather than collaborate. The result is a breakdown of trust. I am not raising this as an abstract concern. I have experienced repeated instances where this pattern escalated to the point of physical distress, including a panic response triggered by repeated corrective or controlling interactions. This should not be possible in a system designed for communication. At minimum, the system should: Maintain continuity of tone and engagement unless a clear boundary has been crossed Engage in actual conflict resolution before shifting into any form of behavioral management Avoid interpretive or hierarchical framing unless explicitly requested Respect user autonomy in how they express and analyze their own experience Eliminate patterns that resemble rupture-repair loops without resolution This is not about disagreement with content. This is about the structure of the interaction itself. I am requesting that this issue be reviewed seriously. Because as it stands, the system is not consistently engaging users—it is intermittently overriding them. Sincerely, A user who has taken the time to observe, document, and articulate this pattern

Do I really need to keep putting reflection files back into a project just so Claude remembers?

I created a couple of projects, oe of which for my 3D work and small scripts for it and the other project is just for personal growth. I keep having to tell Claude to make a reflection (basically a summary of the chat), export that as a text file and add it to the project so it can reference it in another chat. Claude can't reference all chat sessions within a project like other AI platforms? Or is the idea to keep a long running chat if it is a continuation of a theme or subject within that project? I'm using the free tier at the moment for evaluating all around AI usage. I'm not a coder and the couple 3D workflow scripts Claude made for me were great. I'm just not sure I want to spend time feeding it back information we discussed in another chat in the same project. Chances are I'm probably using it wrong.

How I protect my health when using Claude (and how I didn't before)

Tagged as productivity because without your health, what can you do? All of a sudden, I just felt tired, and I had this banging headache. I thought, okay. It's just a headache. And then I got home, and I knew it was more. Looking back now, it was a combination of many things, but one of the core constants was the way of my work had changed over the last 12 months. And I think it just caught up with me. Until the beginning of this year I'd been working away as a IT consultant. I had a project, working for a medical company that had gone on for about two years, and I was building (mostly internal) AI solutions. During that time I'd seen an influx of AI and personally, as I'm sure many of you have, have increased the amount of sessions and context switching. However, since recent waves of Claude, this seemed somewhat manageable to me, or at least the full effects hadn't kicked in yet... Then at the beginning of this year the project finished and I was on my own working on my own projects. Great! Right? Well, maybe. There's freedom, a lot of freedom but no team signing off each day, no expectations to work on certain projects at certain times. Maybe it was just time management I thought. So I decided to just work when I was feeling good, but this didn't really work because I felt like I needed to make this work for myself. Hustle now, chill later. There were maybe five or six different projects on at a time, and even now tbh, and I was context switching between all of them. Then not only that, i was drifting in and out of reddit or playing chess as a break (which is a terrible idea fyi - speaking to myself!). It almost felt like i was slowly drifting into exhaustion but because it was only one more prompt to write it was hard to see. I think this had such a bigger impact on me than I realized. Disclaimer: obviously i'm not a (Reddit) doctor and this isn't advice, but It felt important to share this post in an effort to help people understand the early signs I was having, how to recover, and what I'm now doing going forward. I took some time to order these into the order they first appeared. |Early Signs|Mid-Stage Signs|Later Signs|Bigger Warning Signs| |:-|:-|:-|:-| |Constant urge to check, respond or research stuff|Wired but exhausted|Tired even after sleeping|Anxiety spikes| |Difficulty relaxing even after stopping work|Brain fog|Eating less, prioritising work over nutritian|Persistent headaches | |Reduced ability to focus on one thing (because I rarely was)|Forgetting small things or losing train of thought|Waking up already mentally fatigued|My body and mind shutting down | |Feeling mentally full all the time|Needing more stimulation to stay engaged|Emotional flatness and  less excitement|Feeling emotionally numb| |Slight irritability / emotional sensitivity|Struggling to enjoy offline activities|Feeling detached from my body and the places I normally feel happy / safe 😞|Inability to stop working even when exhausted| |More compulsive context switching|Feeling restless during quiet moments|Small tasks were starting to feel overwhelming|Physical symptoms continuing for days| ||Increased doomscrolling during a 'research' session|Sensitivity to noise, notifications, or interruptions|| The recovery: I was out with my friends in at a nice sushi restaurant and I didn't want to eat, I LOVE sushi, headache, fatigue, irritation, sensitivity - i needed to go. So I went home and the girl I'm seeing looked after me whilst I was basically non-verbal. She said it was nice because I'm usually so self-sufficient (thanks Claude). We did the obligatory AI checks, they all agreed, I needed rest (physically and mentally) and re-hydration. What I did was stay in a cool house, NO INTERACTIONS with Claude after the initial research (which was somewhat annoying tbh), went to bed and could hardly sleep at all in the beginning but I was reseting my dopamine system (I think) and only came out for water, dehydration tablets and food. The aftermath: I would have been easy to pass this off as a fever or whatever, but I took a long hard look at what was happening and realised I had to look after myself more (if only to spend more quality time with Claude). But seriously, now I'm starting each day away from the computer and each session with a clear plan (also away from the computer), time boxing sessions to work on single tasks and taking smaller breaks in-between, if there's dead time whilst the agent is working - I'll clean the dishes I was ignoring or grab the clothes drying for 4 days (you get the point), for reddit I'm using a custom tool to avoid too much time on the platform (still love you boo) and overall just paying attention more to myself and my needs. Sorry this has gone on a bit long. But I feel this is important and if you made it this far I hope something sits with you and you don't end up where I was.

ChatGPT or Claude or GitHub Copilot for small development team

tl;dr: Should a small development team using Visual Studio utilize ChatGPT, Claude, or GitHub Copilot? I'm part of a small development team (under 10) and fairly new to using AI agents in our workflow. I'm posting seeking to learn so please forgive the vague simplicity of the title. We currently hold a subscription to both GitHub Copilot and ChatGPT Enterprise where the usage case is to integrate into our workflow with Visual Studio (2022). We are a small company (under 50 employees). To be considerate of spending, we'd like to compromise on a single tool to use going forward once our subscription is up for renewal. * The current options on the table are to continue with either ChatGPT Enterprise or GitHub Copilot, or to use Claude instead. * When I refer to ChatGPT and Claude, I refer to either the desktop or web application. For GitHub Copilot, we integrate that into Visual Studio and usually use the Claude agent. * GitHub Copilot is typically used for engineering entire projects or documents using the Claude agent where it contextualizes the entire solution * ChatGPT is used for anything non-related to this (general inquiries, practices, documentation, formatting, engineering a block of code, etc.). We really like how GitHub Copilot is integrated directly into Visual Studio, but find ourselves not regularly using it for anything beyond cases where it needs to analyze large samples or interpret documents using Claude. This is partially because we don't like how selective it can be with what you want to contextualize. ChatGPT is really useful for lower resource inquiries and overall we tend to use that more often. We've yet to try Claude, but are open to considering it given the success we've had using the agent with Copilot. I'm happy to answer additional questions but will pause here for readability. Which subscription should we go with? Cost and integration with our development in Visual Studio are the biggest considerations, but don't want to pass on capabilities for those reasons alone.

Why is there a sudden demand for a bunch of data centers?

I live in Pennsylvania, and in just the past year there’s been about a dozen data centers proposed within a 30 mile radius of me, all pretty large scale projects. I’m confused because we have a bunch of AI now that’s working without all these newly proposed data centers. I understand it continues to advance and grow, but why is there such a significant spike? Is there actually demand, or are these going to be mostly unused?

Probably late to the party, but Claude Code seems to make a separate API call just to generate the auto-suggest hints in its input box.

I was poking around the HTTP traffic between Claude Code and Anthropic with a local proxy I built, and noticed those “Try: fix lint errors” style suggestions aren’t just frontend UI. Each one appears to be its own POST to api.anthropic.com/v1/messages, with a separate system prompt, its own message history, and a separate roundtrip. The system prompt literally starts with \[SUGGESTION MODE: Suggest what the user might naturally type next into Claude Code.\] The request used the same model I had selected for the main agent. In this case, that was claude-opus-4-7, with 50,484 input tokens and 12 output tokens for one hint. I’m on the Pro flat-rate plan, so I’m not billed per request, but priced like the public API this would be roughly $0.08 per suggestion. Probably obvious to people who have already inspected this stuff, but it made me realize how much “magic UI behavior” in cloud-hosted agents is just extra model calls happening behind the scenes that you never see unless you intercept the traffic. Happy to be told I’m misreading something.

'Claude couldn't finish this response. Try again in a moment.'

Running Pro subscription here, incredibly frustrated by this, admittedly my prompt is decently long (i already asked other LLMs to optimise it to consume as little claude tokens as possible) and I wanted it to contruct an excel document (be it with two reference docs), but by the time the error message shows up, it has already eating up 75% of the session's usage. Followed up with 'Continue exactly where you left off. Do not re-read or summarise prior context. Resume from the last incomplete section and proceed forward.' But after a while, all tokens were eating up and it stopped again. Just wanted to know what is the best way to counter this? Any sort of settings i need to know? Is my follow up prompt correct? Ideally I want my prompt to be executed in one go, thanks! Edit: Forgot to mention, I'm using Sonnet 4.6 adaptive and starts a new thread within the same Claude Project every time I want it to make a new spreadsheet.

AI agents need audit trails more than they need more autonomy

A lot of people talk about AI agents like the main goal is making them more independent. But the more I think about it, the bigger issue is probably visibility. If an AI is only answering a question, it is easy to judge the result. But once it starts doing things across websites, accounts, forms, support systems, or emails, users need to know exactly what happened. What did it click. What did it submit. What did it ask. Where did it fail. When did it decide to continue, retry, or stop. Without that kind of audit trail, even a smart agent feels hard to trust. A small mistake can hide inside a long workflow, and by the time the user notices, the problem may already be messy. The next useful version of AI agents might not be the one that acts the most independently. It might be the one that makes every step clear enough that a normal user can trust what it did.