One practical email a week for agencies and solo operators running AI agents for clients — what we shipped, what we learned, and the honest tradeoffs. No spam, unsubscribe anytime.
Field notes on running small infrastructure that survives contact with production - the things that break at 2am, and the fixes that hold.
The Google Photos Trap: Why Manipulating Images in the Cloud Isn't What You Think
The Automation Chasm: When Browser Sandboxes, Virtual DOMs, and Local SLMs Collide
Stop Renting One Bloated AI Platform
AI Is the Brain, Automation Is the Muscle
The Ten-Item Menu Theory of Web Design
Drop the Bag: Why We Stopped Building Behind Closed Doors
Reviewing agent-written code like it matters
Your coding agent's best friend is a terminal
Stop Writing Tutorials That Die at Step 3 - Most how-tos die in the first 200 words. Not because the reader is stupid — because the writer wrote for a search engine instead of a human holding a laptop at
Being the Knowledgeable Neighbor: Content That Earns Trust - I got paged at 2:47am last Tuesday because a customer-facing queue depth alarm went off. Not my on-call rotation anymore. A former client, now a friend, called
Write the case study before you write the pitch deck - Your best marketing asset is a log file with a customer's name on it. Not a testimonial. Not a logo wall. A case study written the way an operator writes an inc
Every piece lives at terminalking.com - practical writing for people who run their own machines.
This week, we're diving into the practicalities of running AI services for your clients. From understanding why your AI says what it does, to structuring your client-facing bots, and even optimizing your hosting choices, it's all about making your AI work smarter, not harder.
Ever stared at a customer chat transcript, your blood running cold as you read something utterly off-brand from your AI agent? We've all been there, wondering how it veered so far off course. The good news is, you don't have to guess. You can audit what your AI said and, more importantly, why it said it.
The secret isn't some black box magic; it's about understanding the inputs. When your AI agent produces an answer, it’s reacting to the prompt, the conversation history, and its internal knowledge base. To properly audit, every interaction needs a persistent log. This isn't just the final message; it's the full turn-by-turn: the customer's exact query, the AI's raw output, and any intermediate steps or tools it used. This full context allows you to trace the AI's decision-making process and pinpoint exactly where it went off-script.
Remember that one "AI assistant" you tried a few months back that promised to handle everything for all your clients? You probably ended up spending more time correcting its mistakes than it saved you. The invoices piled up while you babysat the bot, trying to explain again that Client A's social media voice is upbeat, while Client B's is decidedly more corporate.
The problem wasn't the AI itself, but the architecture. You can't ask one general-purpose bot to understand the nuances of multiple, distinct businesses. That’s like asking a single virtual assistant to be an expert in both neurosurgery and competitive dog grooming. It just doesn't scale well and leads to messy output and endless frustration. The plain truth is: your agency needs one dedicated bot per client. This allows for hyper-specific training, tailored knowledge bases, and a unique voice for each client, leading to far more accurate and valuable interactions.
Remember that gut punch when your monthly cloud bill for a simple AI agent came in at double what you expected? Or the way your local dev machine groaned to a halt trying to run a seemingly small model? We've all been there, staring at a usage graph wondering if we just signed up for a second mortgage.
It usually boils down to one thing: matching the right engine to the job. For AI, that means understanding when to splurge on a GPU cloud, and when a trusty CPU will do the trick. The common wisdom is "AI needs GPUs!" And yes, for training big models, those beasts are non-negotiable. But for inference – actually using a pre-trained model – it's a whole different ballgame. Many smaller, optimized models can run perfectly well on modern CPUs, especially if you're not processing thousands of requests per second. Evaluate your actual workload: if it's not real-time, high-throughput, you might be throwing money away on GPU instances when a CPU-based solution would be far more cost-effective.
For more insights and tools to streamline your AI services, explore hub.sqs.chat.
Tired of pulling AI agent prices out of thin air? Here’s a better way than hoping for the best.
Most agencies price AI services like they do web design: a flat fee based on project scope. But AI agents are different. They run continuously. Think about it like a subscription, not a one-off build. Your client isn't just paying for the setup; they're paying for the ongoing value the agent delivers. That value comes from its usage.
So, ditch the flat fee for agent deployments. Instead, adopt a hybrid model. Charge a reasonable setup fee for the initial build and customization – this covers your time and expertise. Then, implement a recurring usage-based fee. This could be per transaction, per interaction, or even based on the volume of data processed. This aligns your income directly with the value your agent provides, and it's transparent for the client. They pay more as they get more from the agent.
For example, if you build a customer support AI agent, your setup fee covers training on their knowledge base. Then, charge per resolved query. Or for a lead qualification agent, charge per qualified lead passed to their sales team. This isn't just fair; it’s a powerful selling point. You’re telling them: "You only pay more when you win more."
Actionable Tip: Calculate your base setup fee, then determine a per-unit value that makes sense for your client's business. Offer a tiered usage model for scalability.
We run these agents ourselves; see our plans at hub.sqs.chat.
Field notes on running small infrastructure that survives contact with production - the things that break at 2am, and the fixes that hold.
The Google Photos Trap: Why Manipulating Images in the Cloud Isn't What You Think
The Automation Chasm: When Browser Sandboxes, Virtual DOMs, and Local SLMs Collide
Stop Renting One Bloated AI Platform
AI Is the Brain, Automation Is the Muscle
The Ten-Item Menu Theory of Web Design
Drop the Bag: Why We Stopped Building Behind Closed Doors
Reviewing agent-written code like it matters
Your coding agent's best friend is a terminal
Stop Writing Tutorials That Die at Step 3 - Most how-tos die in the first 200 words. Not because the reader is stupid — because the writer wrote for a search engine instead of a human holding a laptop at
Being the Knowledgeable Neighbor: Content That Earns Trust - I got paged at 2:47am last Tuesday because a customer-facing queue depth alarm went off. Not my on-call rotation anymore. A former client, now a friend, called
Write the case study before you write the pitch deck - Your best marketing asset is a log file with a customer's name on it. Not a testimonial. Not a logo wall. A case study written the way an operator writes an inc
Every piece lives at terminalking.com - practical writing for people who run their own machines.
Field notes on running small infrastructure that survives contact with production - the things that break at 2am, and the fixes that hold.
The Google Photos Trap: Why Manipulating Images in the Cloud Isn't What You Think
The Automation Chasm: When Browser Sandboxes, Virtual DOMs, and Local SLMs Collide
Stop Renting One Bloated AI Platform
AI Is the Brain, Automation Is the Muscle
The Ten-Item Menu Theory of Web Design
Drop the Bag: Why We Stopped Building Behind Closed Doors
Reviewing agent-written code like it matters
Your coding agent's best friend is a terminal
Stop Writing Tutorials That Die at Step 3 - Most how-tos die in the first 200 words. Not because the reader is stupid — because the writer wrote for a search engine instead of a human holding a laptop at
Being the Knowledgeable Neighbor: Content That Earns Trust - I got paged at 2:47am last Tuesday because a customer-facing queue depth alarm went off. Not my on-call rotation anymore. A former client, now a friend, called
Write the case study before you write the pitch deck - Your best marketing asset is a log file with a customer's name on it. Not a testimonial. Not a logo wall. A case study written the way an operator writes an inc
Every piece lives at terminalking.com - practical writing for people who run their own machines.
Running an AI service or self-hosted tool comes with unique challenges, from understanding agent behavior to managing your infrastructure. This week, we're diving into practical strategies to maintain control, ensure reliability, and make informed decisions about your AI stack.
That pit-in-your-stomach feeling when an AI agent says something off to a client? It’s not just about what was said, but why it was said. If you don't know the why, you can't fix it, and that erodes trust quickly.
The good news is you don't have to guess. You can audit your AI agent conversations to understand the decision-making process. Think of it like pulling up the tape on a bad play – not to shame, but to learn.
First, log everything. Every interaction, every query, every response. Many systems only log the final output, but you need the full transcript, timestamps, and crucially, the context the agent was operating under. What was the prompt? What data did it access? This comprehensive logging is the foundation for effective auditing.
The thought of building and running your own AI agent, perhaps even on a beefy Mac Mini, is tempting. You'd control everything, which sounds ideal. But let's talk about what that really costs beyond the hardware.
You could run your own agent locally. That control is great until a model update breaks your custom scripts at 2 AM, or your internet goes out, or a power flicker corrupts your local database. Then you're debugging, restoring backups, and losing billable hours. That shiny new Mac Mini starts to look less like an asset and more like a full-time job.
Managed AI services handle these operational headaches. They provide the infrastructure, ensure uptime, manage updates, and secure your data. While there's a recurring cost, it frees you to focus on developing and delivering value to your clients, rather than becoming an IT manager for your AI.
Remember that sinking feeling when your meticulously crafted AI agent suddenly choked on a slightly malformed CSV? Or the late-night scramble when a dependency update broke everything? Beneath the shiny UIs and impressive capabilities of AI, there’s a whole lot of plumbing.
When we talk about a managed cloud stack for AI agencies, we're not just spinning up a VM and wishing you luck. We're talking about taking those gut-wrenching moments off your plate. This includes reliable orchestration, ensuring your agent can consistently talk to APIs, databases, and other agents without interruption. It means handling the complex dance of dependencies, updates, and scaling so you don't have to.
For more insights and tools to streamline your AI operations, explore hub.sqs.chat.
Hey there, fellow agency owner!
Ever feel like you’re drowning in the “should-do” tasks instead of the “must-do” ones? This week, let's offload some of that mental weight. AI agents aren't just for clients; they’re your secret weapon for reclaiming time.
Here are three business tasks you can genuinely hand off to an AI agent this week:
1. Prospect Research & Lead Qualification: Instead of sifting through LinkedIn profiles for hours, train an AI agent to identify ideal client profiles based on specific criteria (industry, company size, tech stack, pain points). Give it a list of company names, and it can even pull relevant news or recent funding rounds, flagging only the truly hot leads. Imagine getting a daily digest of qualified prospects, ready for your personalized outreach.
2. Content Repurposing: Got a recent blog post or client case study? Feed it to an AI agent. Instruct it to generate 5-10 social media posts (LinkedIn, X, Instagram captions), 3 email newsletter blurbs, and even a short video script summary. This isn't just about speed; it's about maximizing the reach of your valuable content without you lifting a finger beyond the initial prompt.
3. Client Feedback Summarization & Sentiment Analysis: If you run surveys, collect testimonials, or have client communication logs, an AI agent can be a game-changer. Upload the raw data and ask it to summarize key themes, identify common pain points, and even gauge overall sentiment. This provides quick, actionable insights you can use to improve services, spot upsell opportunities, or proactively address issues.
Actionable Tip: Pick one of these tasks, define the outcome clearly, and build a simple AI agent for it today. Don't overthink it; just start.
We run these agents ourselves to keep our operations lean. See how at hub.sqs.chat.
Field notes on running small infrastructure that survives contact with production - the things that break at 2am, and the fixes that hold.
The Google Photos Trap: Why Manipulating Images in the Cloud Isn't What You Think
The Automation Chasm: When Browser Sandboxes, Virtual DOMs, and Local SLMs Collide
Stop Renting One Bloated AI Platform
AI Is the Brain, Automation Is the Muscle
The Ten-Item Menu Theory of Web Design
Drop the Bag: Why We Stopped Building Behind Closed Doors
Reviewing agent-written code like it matters
Your coding agent's best friend is a terminal
Stop Writing Tutorials That Die at Step 3 - Most how-tos die in the first 200 words. Not because the reader is stupid — because the writer wrote for a search engine instead of a human holding a laptop at
Being the Knowledgeable Neighbor: Content That Earns Trust - I got paged at 2:47am last Tuesday because a customer-facing queue depth alarm went off. Not my on-call rotation anymore. A former client, now a friend, called
Write the case study before you write the pitch deck - Your best marketing asset is a log file with a customer's name on it. Not a testimonial. Not a logo wall. A case study written the way an operator writes an inc
Every piece lives at terminalking.com - practical writing for people who run their own machines.
This week, we're diving into the practicalities of running AI services for clients. From understanding your AI's decisions to securing client data and pricing your expertise, these insights are designed to help you build a more robust and transparent AI offering.
Ever get that cold sweat when an AI agent handles a customer, and you just know something went sideways? The customer’s email comes in, terse and unhappy, and you’re left wondering, "What on earth did it tell them?" Or worse, "Why did it say that?"
This isn’t about catching an AI in a lie; it’s about understanding its decision process. If you don't know why it acted a certain way, you can't fix it. And leaving unhappy customers in its wake? That’s just flushing revenue.
Here’s how we get past the black box:
First, log everything. Every single interaction, every prompt, every response. We’re talking full transcripts, not just summaries. Your managed AI agent should be doing this automatically. If it's not, that's your first red flag. For self-hosted setups, this means configuring robust logging.
Remember that sinking feeling when a client asks for a quote on an "AI project" and your gut just screams, "How do I even begin to price this?" We’ve all been there. The hype around AI is deafening, but the practical steps for turning that into predictable revenue for your agency? That's often a whisper.
The secret isn't a fancy new pricing model. It’s understanding the actual value delivered, not the tech stack. What does AI mean for your clients? It means predictable outcomes, faster results, and less wasted effort.
Here's how we approach it:
Remember that moment you accidentally replied-all to an email chain meant for one person? Now imagine that, but with your client's proprietary data, shared across every project, visible to every other AI model you're running. Gives you chills, right?
We've all been there, trying to squeeze every ounce of efficiency from our tools. And sure, running all your client projects through one big, shared AI account feels efficient at first. One billing statement, one login, one less thing to manage. But I've seen too many agencies get burned by this "shortcut," and it usually comes down to two things: data commingling and cost creep.
Think about it. If Client A's confidential market research is being processed by the same model that's generating ad copy for Client B's competitor, you have a serious ethical and security problem. Beyond that, a shared account makes it nearly impossible to accurately attribute costs or debug issues for a specific client.
For more insights and tools to streamline your AI operations, explore the resources available at hub.sqs.chat.
AI isn't magic. So why do clients pay for agents? They’re buying a future where their business runs smoother, faster, and smarter.
Think of it this way: when a client hires an AI agent, they’re not just getting code. They're acquiring a tireless, dedicated employee who never takes a coffee break, never complains, and works 24/7. This agent can answer customer queries instantly, qualify leads perfectly, or even draft personalized emails at scale. The real value is the impact this digital employee has on their bottom line – reduced costs, increased sales, or improved customer satisfaction. It's about solving a tangible business problem with an invisible workforce.
My favorite example is a small e-commerce store using an AI agent for first-line customer support. Before, their owner spent hours answering repetitive questions. Now, the AI handles 80% of those, freeing the owner to focus on product development and marketing. The client didn't buy "an AI chatbot"; they bought their time back and happier customers. That's the core offering.
Actionable Tip: When pitching, don't just list features. Articulate the direct business benefit. Instead of "our AI uses natural language processing," say "our AI saves you 10 hours a week on customer service, letting you focus on growth."
We run these agents ourselves at hub.sqs.chat – check out our plans to see them in action.
This week, let’s talk about a thorny AI service question: who pays for the API keys, and more importantly, who owns the client data that passes through them?
Many of us are building awesome AI solutions for clients, but the underlying LLM access usually comes via API keys from providers like OpenAI. You have two main options: "bring your own key" (BYOK) where the client pays directly, or "pooled keys" where you, the agency, pay for a bulk account and charge them. BYOK seems simple, right? Client pays, client owns. But what about all the data that flows through that key? If you’re processing sensitive client information, even with BYOK, you’re still handling it.
Pooled keys offer more control for you. You can monitor usage, potentially get better rates, and bake the cost into your service fee. The catch? You're now the gatekeeper for all that data. If something goes wrong, or the client wants their data back, you’re on the hook. And what if they decide to leave? That data, processed through your key, is technically yours to manage – and delete securely.
Actionable Tip: For any new AI service, draft a clear "Data & API Key Ownership Addendum" to your client agreement. Specify who provides the key, who pays for usage, and crucially, what happens to the data (including prompts and responses) if the client churns or requests deletion.
Ensure your contracts clearly define data ownership and responsibilities. We navigate these complexities daily with our own AI agents.
Field notes on running small infrastructure that survives contact with production - the things that break at 2am, and the fixes that hold.
The Google Photos Trap: Why Manipulating Images in the Cloud Isn't What You Think
The Automation Chasm: When Browser Sandboxes, Virtual DOMs, and Local SLMs Collide
Stop Renting One Bloated AI Platform
AI Is the Brain, Automation Is the Muscle
The Ten-Item Menu Theory of Web Design
Drop the Bag: Why We Stopped Building Behind Closed Doors
Reviewing agent-written code like it matters
Your coding agent's best friend is a terminal
Stop Writing Tutorials That Die at Step 3 - Most how-tos die in the first 200 words. Not because the reader is stupid — because the writer wrote for a search engine instead of a human holding a laptop at
Being the Knowledgeable Neighbor: Content That Earns Trust - I got paged at 2:47am last Tuesday because a customer-facing queue depth alarm went off. Not my on-call rotation anymore. A former client, now a friend, called
Write the case study before you write the pitch deck - Your best marketing asset is a log file with a customer's name on it. Not a testimonial. Not a logo wall. A case study written the way an operator writes an inc
Every piece lives at terminalking.com - practical writing for people who run their own machines.