What separates an AI agent that survives contact with a real business from an impressive demo, and the guardrails that make one safe to deploy.
·4 min read
An AI agent is a system that takes multiple steps toward a goal without being
prompted at each one. It decides what to do next, uses tools, and keeps going
until the task is done or it gets stuck.
Demos of this are extraordinary. Production deployments are frequently
disappointing. The gap between the two is not model quality. It is engineering.
Why demos succeed and deployments fail
A demo runs once, on a chosen example, with someone watching. Production runs
hundreds of times on inputs nobody anticipated, with nobody watching.
The specific things that break:
Compounding error. An agent taking ten steps at 95 percent reliability per
step succeeds about 60 percent of the time. At twenty steps it is under 40
percent. Reliability that sounds excellent per step is unusable end to end.
No stopping condition. An agent that cannot recognise it is stuck will
retry, rephrase, and burn tokens indefinitely. Every agent needs a step budget
and a way to give up honestly.
Silent wrongness. A crashed process is obvious. An agent that confidently
files the wrong data is not, and may not be noticed for weeks.
Changing ground. The API it depends on changes a field name and the agent
does something subtly wrong rather than failing loudly.
What makes an agent deployable
Fewer steps. The single biggest reliability lever. If a task can be done in
three steps rather than twelve, the maths improves dramatically. Much of
building good agents is removing the need for the agent to decide things.
Deterministic where possible. If a step can be plain code, make it plain
code. Use the model for the parts that genuinely need judgement, and use code
for the parts that do not. An agent that calls a well-tested function is far
more reliable than one reasoning its way through the same logic.
Hard limits. A step budget, a time budget, and a token budget. All three,
enforced by the harness rather than requested in the prompt.
Approval gates on consequences. Reading is cheap to get wrong. Sending,
deleting, paying and publishing are not. Anything with an outward-facing or
irreversible effect should pause for a human, at least until you have evidence
it is safe.
Observability. Log every tool call, every input, every output. When someone
asks “why did it do that,” you need an answer. This is not optional in any
business context.
Evaluation. A set of cases with known-correct outcomes, run whenever
anything changes. Without it you are guessing whether a prompt change helped or
hurt, and prompt changes routinely do both at once.
Where agents genuinely pay off
Triage and routing. Read the incoming thing, classify it, route it. Errors
are cheap because a misrouted item gets forwarded.
Research and summarisation across sources. Pull from several systems and
assemble a summary. The human reviews the result, so mistakes surface.
Structured extraction at volume. Invoices, applications, forms. Genuinely
hard with rules, genuinely suited to models.
First-pass drafting. Draft the reply, the description, the report. A person
edits before it goes out.
Notice the pattern: a human sees the output before it matters. That is the
common thread in agent deployments that work.
Where they usually should not go
Anything requiring exact arithmetic. Anything where a wrong answer is a
regulatory or safety problem. Anything replacing a process nobody has written
down, because the agent will automate the confusion.
And anything where the honest answer is a scheduled job. A great deal of what
gets proposed as agent work is a cron job with extra steps and a much larger
bill.
The honest cost picture
Agents cost more to run than single requests, because they make many calls per
task. The two things that inflate cost most are unnecessary steps and using the
largest model for steps a smaller one handles fine.
A well-scoped agent doing genuine work for a small business is usually tens of
dollars a month, not thousands. If a quote is dramatically higher, ask what is
driving the step count.
Start with the boring version
Before building an agent, build the thing without one. Write the script that
does the task in a fixed order. Frequently that is enough, and it is cheaper,
faster and more reliable.
If the fixed order genuinely cannot handle the variety of real inputs, that is
your evidence an agent is warranted, and you now have a baseline to measure it
against.
If you want a straight assessment of whether your problem needs an agent, a
simple automation, or nothing at all,
describe what you are trying to do.
One email when something new goes up, roughly twice a month. Plain
writing on what software costs and what is worth building. No sequences,
no sales calls, and one click to leave.
Tell us what the problem is. You will get an honest read on whether it is worth building, what it would take, and roughly what it would cost. No pitch deck, no pressure.
Six things: marketing websites, web applications, internal tools, AI systems, AI agents and MCP servers, and database work. Most projects touch two or three of them rather than sitting neatly in one.
Yes. Marketing sites, landing pages and content platforms, built on modern static tooling rather than page builders. They load in under a second and have the technical SEO groundwork done properly.
Yes. Customer portals, dashboards, booking systems and SaaS products. The kind where users log in, data persists and money changes hands, so it has to be right.
Software your staff use rather than your customers. A dashboard pulling numbers from systems that do not talk to each other, a job tracker matching how you actually work, or a scheduled job replacing a report someone assembles by hand every Monday.
Yes, and we will tell you when it is the wrong tool. Retrieval over your own documents, support assistants, document extraction and classification, all built with evaluation and guardrails from the start.
MCP is the standard that lets AI assistants reach your real systems instead of guessing. We build MCP servers that expose your data and tools to Claude and other clients, and agents that carry out multi-step work with permissions and audit trails.
Yes. Schema design, query performance work, migrations between systems, ETL pipelines and reporting layers. Usually called in when the database has quietly become the bottleneck, or the risk.
Not native iOS or Android apps. We build web applications that work properly on phones, which covers most of what businesses actually need. If you need a true native app, we will say so rather than talk you into a compromise.
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We design and build the sites and applications we make. We do not do standalone brand identity, logo design or print work. If you already have brand guidelines we will follow them.
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Often, yes. The first step is a short paid audit of the existing code and infrastructure, so you get an honest assessment of what is salvageable and what it would cost either way before committing to anything.
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Yes, if you want it. You can also take the project and run it yourself using the documentation provided. Both are genuine options and there is no pressure toward a retainer you do not need.
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A marketing site typically lands between $3,000 and $12,000 depending on page count and complexity. You get a written fixed price before any work begins, so the number you approve is the number you pay.
Applications and internal tools are scoped individually and most start around $15,000. Once users log in and data persists, you are buying software rather than a website, and it is priced like software.
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A single automation or integration is usually $2,000 to $6,000. A proper internal application with its own database and user roles runs $6,000 to $20,000. Anything your operations depend on goes up from there.
Fixed price wherever the scope can be fixed. That means we absorb the overrun rather than you, and it is usually worth the small premium on a first project together. If something changes mid-build you hear about it before the work happens, not on the invoice.
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A static marketing site is commonly free to host and stays free at traffic levels most local businesses never reach. A well-built internal tool usually runs $10 to $50 a month. Budget 15 to 20 percent of the build cost annually for maintenance.
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Copywriting, photography, ongoing SEO work and third-party subscriptions are the four things most commonly assumed by the client and excluded by the quote. Ours states what is and is not covered explicitly.
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Yes, typically a portion up front with the remainder on delivery, or split across milestones for larger projects. The exact split is agreed in the written proposal before work starts.
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Rarely page count. Integration complexity, how much the process changes mid-build, and how clean your starting data is. Messy historical data is the single most common reason projects overrun.
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Usually. Tell us the number and we will tell you honestly what fits inside it and what does not. That is a more useful conversation than pretending everything fits.
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Yes. Scoping conversations and written proposals cost nothing and carry no obligation. We will also provide a written quote for a grant application without charging for it.
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Yes, and they are meaningful. Registered charities, non-profits and small local businesses get reduced rates on every service, because a tight budget should not be the reason an organisation is stuck with software that does not work.
Registered charities and the volunteer-run groups that support them, non-profits and community organisations, and small owner-operated businesses including new ventures still finding their feet.
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There is no application and no paperwork. Mention it in your first message and the reduced rate is built into the quote you receive.
It depends on the project and the organisation, and you are told exactly what it is before committing to anything. We do not publish a single percentage because it would be either misleading or useless.
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Often, yes, and they are widely underused. Google for Nonprofits, TechSoup Canada, community foundations and your existing funders are all worth approaching. We wrote a guide to what is actually available.
If you are small and owner-operated, yes. Ask when you get in touch and we will tell you straight rather than making you guess.
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Four stages. Scope: a conversation about the problem, not the tech. Plan: a written proposal with price and timeline. Build: short cycles with something working at the end of each one. Ship and support: deploy, hand over documented code you own outright.
A straightforward marketing site takes two to four weeks from kickoff. Applications and internal tools usually run six to twelve weeks, delivered in cycles so you see something working every week rather than waiting until the end.
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Completely. Code lives in your repositories, hosting and domains are registered in your name, and everything is documented at handover. There is no licence to keep paying and no lock-in forcing you to stay.
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You keep everything, because it was always in your accounts. Documentation and a clean handover are part of the work rather than an extra, precisely so that another developer can pick it up.
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Most of it is at the start, agreeing what gets built. After that, expect a short check-in each cycle to look at progress. The most common cause of delay is waiting on content from the client side, so having copy and images ready helps more than anything else.
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Yes, and it is normal. You hear the cost and timeline effect before the work happens, so a change is a decision you make rather than a surprise on the invoice.
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Yes. Content is set up so you can change text and images without a developer in the loop, using either a CMS or markdown depending on what suits your team.
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Yes, routinely. Send yours over or ask and we will provide one.
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Often. Being brought in for a specific piece, or to work alongside an in-house team, is common and works fine as long as the boundaries are clear up front.
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A description of the problem. Not a specification, not a wireframe, just what is going wrong or what you are trying to do. Everything else comes out of the scoping conversation.
Astro and Next.js for sites and applications, React and TypeScript on the front end, Node.js and Python on the back, PostgreSQL and MongoDB for data, and Netlify or Vercel for hosting. Chosen per project rather than by habit.
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Only when it is genuinely the right answer, which is less often than it is used. For most marketing sites a static build is faster, safer and cheaper to run. For a large store or complex memberships, WordPress earns its place.
Wherever you need it. AWS and Google Cloud both have Canadian regions in Montreal and Toronto, and hosting in Canada is straightforward and usually costs nothing extra. If your customers ask where their data lives, that is worth having a clear answer to.
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Usually not. PIPEDA does not require personal data to be stored in Canada, provided you are transparent and the data receives comparable protection. Rules that do mandate it are mostly provincial public-sector rules. Health data and government contracts are stricter, so check those specifically.
Yes. Sites are built to WCAG 2.1 AA, which covers keyboard navigation, screen readers, colour contrast and focus states. It is part of the build rather than a paid extra.
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Sub-second loads and 95 or better on Lighthouse are the target, and normally the result. Static output with no render-blocking JavaScript is most of how that is achieved.
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The technical groundwork, yes: structured data, sitemaps, clean semantics, fast pages and unique metadata per page. Ongoing content and link building is a different discipline and we will say so rather than sell it.
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A static site has no database to breach, no admin login to brute force and no plugins running server-side code, which removes most of the attack surface. Where an application does need those, authentication and permissions are built to hold up.
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Usually. If your CRM, accounting package or booking system has an API or webhooks, it can be connected. Integration between systems you already pay for is one of the highest-value things to automate.
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Backups you have actually tested, not just configured. Restoring from a backup nobody has ever tried is a plan with an unknown failure rate.
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Often not, and we will say so. It is worth it when the task involves unstructured language, the rules are fuzzy, you can tell right from wrong, and a wrong answer is survivable. Fail any of those and something simpler is the better build.
Answering questions over your own documents, extracting structure from messy input like invoices and forms, triaging and routing incoming work, and generating first drafts a human then edits. Those four cover most of the real value.
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Chatbots on the front page, which almost nobody wants. Anything needing exact numbers, because models are not calculators. And automating a process nobody has written down, which just scales the confusion.
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Less than most people expect. A well-built retrieval system for a small business commonly runs in the tens of dollars a month. Costs balloon from stuffing whole documents into every request and using the largest model for tasks a smaller one handles.
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It can, which is why systems are built to answer from your data with sources attached rather than from memory. Anything that matters gets a human in the loop, and evaluation is built in from the start so you know when it works.
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A system that takes multiple steps toward a goal without being prompted at each one. Impressive in demos, frequently disappointing in production, and the gap is engineering rather than model quality.
Worth it when your team already uses an AI assistant daily, the information lives in a system with an API, and people ask the same questions repeatedly. Start with one read-only server answering a single question, which takes days and cannot break anything.
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It is, when scoped properly. Expose the narrowest set of capabilities that answers the real questions, separate read from write, put approval gates on anything destructive, log every call, and give it its own credentials rather than a shared admin account.
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Call, email, or use the contact form. The form asks a few questions up front so the first reply can be useful rather than a request for more information.
Within one business day, usually sooner. If it is urgent, calling is faster than the form.
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Alberta, Canada. Work happens remotely with clients across Canada and the United States, over email, video calls and shared preview links.
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Regularly. Location has never been a constraint on this kind of work. Being Canadian helps with timezone overlap, invoicing in Canadian dollars and familiarity with the privacy rules, but it is not the deciding factor.
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That is the preferred starting point. No pitch deck, no pressure, and no obligation. You will get an honest read on whether the thing is worth building at all.
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That is the normal case and completely fine. Describe what is going wrong or what takes too long, and working out the shape of the solution is part of the job.
