Automation
You define every step in advance. When this happens, do that. Reliable, predictable, and blind to anything you did not anticipate.
Good forHigh-volume, unchanging steps where the rules never vary and being wrong is expensive.
Sites that load fast and convert
Custom builds and integrations
Bounded, auditable, human-approved
Being found, staying fast, staying safe
An assistant answers questions. An agent takes actions — reads a system, decides what to do, does it, and checks the result. For Toronto and GTA businesses that means the repetitive work between your tools gets handled, with a record of every decision and a human in the loop wherever it matters.
The interesting part is not the model. Any competent build can call one. The work is deciding what the agent is allowed to touch, what it must ask about first, how it proves what it did, and what happens when it is wrong — because it will occasionally be wrong, and a system designed on the assumption that it will not is the one that causes damage.
Agent, assistant or automation? ↓Definitions
These get used interchangeably in sales material, and the differences decide what you should actually build.
The distinction that matters commercially is who decides. Automation follows a fixed path you designed. An assistant produces output a person then acts on. An agent decides its own next step within limits — which is more capable and correspondingly more work to make safe.
You define every step in advance. When this happens, do that. Reliable, predictable, and blind to anything you did not anticipate.
Good forHigh-volume, unchanging steps where the rules never vary and being wrong is expensive.
Understands a request and produces something — a draft, a summary, a suggested reply. A person reviews it and decides what to do.
Good forDrafting, summarising and triage, where judgement stays with a person and speed is the win.
Given a goal, works out the steps itself, uses the tools it has been given, checks whether it worked, and retries or escalates.
Good forMulti-step tasks across several systems where the path varies by case and a person is the bottleneck.
The stack
Nothing here is proprietary to us. Models are swappable by design — the agent's logic, prompts and tools live in your repository, so changing provider is a configuration change rather than a rebuild.
Models
Framework & data
Where agents plug in
Applications
All six share a shape: a task that repeats, spans more than one system, and currently waits on a person who has better things to do.
Reads incoming enquiries, works out what is being asked, enriches from your CRM, and routes with a suggested reply attached.
Replaces: the first-pass read of every email
Pulls information from several sources, cross-checks it, and produces a short briefing with links back to where each claim came from.
Replaces: an afternoon of tab-hopping
Watches for orders that need attention — address mismatches, stock problems, payment failures — and either resolves them or escalates with context.
Replaces: manually scanning for problems
Takes invoices, forms and PDFs, extracts what matters, validates against your records, and files them where they belong.
Replaces: retyping data between systems
Answers staff questions from your documentation, policies and past tickets — grounded in your material rather than general knowledge.
Replaces: interrupting the person who knows
Tracks quotes, invoices and enquiries that have gone unanswered, and follows up on a schedule with context from the original thread.
Replaces: the follow-ups nobody gets to
Mechanics
A worked example: a customer emails asking to change a delivery address on an order already placed. Step through what the agent does — and where it stops.
Notice step five. The agent is capable of making the change itself, and deliberately does not. Where the boundary sits is a business decision, not a technical one — and it is the conversation worth having before anything gets built.
Safety
An agent with unrestricted access to your systems is a liability regardless of how good the model is. These are the constraints that turn capability into something you can put into production.
Most of this work is not AI work at all. It is ordinary software engineering — permissions, validation, logging, rollback — applied to a component that behaves less predictably than the rest of your stack.
The agent can only call functions you have given it. It cannot browse your database or invent an action. Everything it can do is enumerable and reviewable.
It runs as its own account with the narrowest permissions that let it work — read-only where possible, write only on the specific records it should touch.
Actions above a value, outside a pattern, or touching a flagged account stop and wait for a person. You choose where those lines sit.
Every input, decision, tool call and output recorded. When someone asks why a customer got that email, there is an answer.
Actions are undoable where the underlying system allows it, and the agent records what it would take to reverse anything that is not.
Caps on steps, tool calls and spend per task, so a confused agent stops rather than retrying expensively for an hour.
Answers come from your documents and records rather than the model's general knowledge, which is how you avoid confident invention.
Tested against a set of real past cases with known correct outcomes, so accuracy is measured rather than assumed.
One control that stops the agent immediately, available to people who are not developers, tested before go-live.
Honesty
A meaningful share of the enquiries we get about agents are better solved another way, usually more cheaply. Saying so early costs us work and saves you considerably more.
If the path is genuinely fixed, a scripted automation is cheaper, faster and more reliable. Reasoning is only worth paying for when the path changes by case.
Moving money, submitting filings, altering medical or legal records. An agent can prepare these; a person should commit them.
Building, testing and maintaining an agent is real work. Below a certain frequency the arithmetic simply does not close.
An agent cannot answer from documentation you have not written. Sometimes the useful first project is writing it down.
If the process itself is the problem, an agent makes the wrong thing happen faster. Fix the process, then decide.
Anything requiring relationship context, negotiation or care. The agent can prepare the ground and stop there.
Scope
Roughly a fifth of the effort is prompting and model work. The rest is the engineering that makes it dependable.
Sitting with whoever does the task now and writing down what actually happens — including the exceptions that never made it into any document.
Building the specific functions the agent can call, each validated, each doing one thing, each safe to call twice by mistake.
Connecting your documents and records so answers come from your material, with retrieval tuned against real questions.
Instructions, examples and refusal rules, kept in version control so changes are reviewable rather than pasted into a text box.
Real past cases with known outcomes, run against every change so you can see whether an adjustment helped or quietly hurt.
The interface a person uses to approve, correct or take over — designed for the people who will actually use it.
Dashboards for volume, escalation rate, cost per task and where it gets stuck. Without these you cannot tell whether it is working.
Running reliably, retrying transient failures, with model changes rolled out behind a switch rather than in place.
Documentation of what it does and why, plus a walkthrough for the people supervising it day to day.
Questions
A chatbot responds. An agent acts. A chatbot can tell a customer your return policy; an agent can check the order, confirm eligibility, create the return in your system and email the label — then log what it did.
That capability is why the guardrails matter. A chatbot giving a poor answer is embarrassing; an agent taking a poor action has consequences.
Less than most custom software and more than a subscription tool. The variable is how many systems it touches and how much your process varies by case — a well-defined task against one system with a good API is a modest build.
There is also a running cost: model usage per task, typically small per run but worth modelling against volume before committing.
Whichever suits the task, and the architecture is built so that can change. Different models are better at different things and the landscape shifts every few months, so being locked to one provider is a liability rather than a feature.
The prompts, tools and evaluation set are yours and portable. Swapping the model underneath is a configuration change.
It will, occasionally — that is a property of the technology rather than a defect to be engineered away entirely. The design assumption is that mistakes happen, so the questions are how quickly you notice, how much damage is possible, and how easily it is reversed.
Approval thresholds, action limits, audit logs and an off switch are what keep the answer to all three comfortable.
Not under the business terms of the major providers, which exclude API data from training by default. We configure accordingly and can show you the setting.
Where data is genuinely sensitive, options include redaction before it leaves your systems, or running a smaller open model on infrastructure you control. That costs more and is sometimes the right answer.
Usually. If a system has an API, the agent can use it. If it does not, the same techniques that apply to any integration apply here — scheduled exports, a database connection, occasionally something more creative.
This is worth checking early, because it is the assumption most likely to change a project's shape.
A narrow first agent — one task, one or two systems — is usually weeks. We deliberately start narrow, because the first version teaches you things about your own process that no amount of planning would have surfaced.
Broadening comes after, informed by real usage rather than assumptions.
The people supervising it do, and that training is part of the build. They need to know what it can and cannot do, how to read its log, how to correct it, and how to stop it.
Everyone else generally notices only that something they used to wait for now happens sooner.
Call back
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