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Development · AI Agents

AI Agents Tested Before They Go Live

A prototype that answers one question is easy. Keeping an agent reliable over thousands of conversations takes tool access, grounded retrieval, safety checks, testing and tracing. CreateITivity builds those layers so the agent can close tickets, book appointments and move work between systems.

  • Acts in your systems through typed tools and MCP
  • Passes uncertain cases to a person
  • Every release tested, every conversation traced
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What kind of AI agents does CreateITivity build?

CreateITivity designs and deploys AI agents for Canadian businesses: customer support, sales research, knowledge assistants over your own documents, and voice. Each agent works through defined tools (including MCP) so it can act in your systems, and it ships with retrieval, guardrails, test suites and tracing so it can run without someone watching it.

  • Works with Claude, GPT or open-weight models, picked for each job on cost and speed
  • Hands a conversation to a person when its confidence drops below a set threshold
  • Can be deployed in your own cloud account, including Canadian regions
  • Code, prompts, evaluation suite and infrastructure belong to you
Agent types

The agents we build most often

Pick the workload that costs your team the most hours. Each card explains how that agent gets scoped and launched.

Custom AI Agents

Tools, function calls and orchestration

Agents designed for your business that work with your data and take actions in your systems.

See the approach

Customer Support Agents

Fewer tickets, clean escalations

Support agents that answer routine tickets from your own documentation and pass the rest on with context.

See the approach

Sales & SDR Agents

Qualify, research and follow up around the clock

Agents that sort inbound leads, research accounts, draft follow-ups and book meetings in your calendar.

See the approach

RAG Knowledge Assistants

Sourced answers, honest about gaps

Assistants that search your knowledge base, cite their sources and say so when they do not know.

See the approach

Voice Agents

Real-time voice with short pauses

Voice agents for incoming and outgoing calls that book, triage, qualify and support.

See the approach

Workflow Automation

Multi-step tasks across your tools

Agents and pipelines that handle repetitive work across your systems, from triage and data entry to reports and admin.

See the approach

LLM & MCP Integration

Bring LLMs into your product and tools

Add LLMs and tool use to your product or internal systems, including MCP servers that give agents controlled access.

See the approach

Agent Evals & Monitoring

Testing, guardrails and visibility

The safety layer: test suites, guardrails, tracing and cost and speed monitoring that keep agents reliable.

See the approach
Under the hood

Inside a single agent run

The agent plans, calls a tool, reads the result and repeats, with checks wrapped around each step.

  1. Requestticket · chat · call · trigger
  2. Agent reasonsplan → act → observe
  3. ToolsRAG · MCP · your APIs
  4. Guardrails + evalscheck · handoff · trace
  5. Action + answerlogged & measured
Rough numbers

How much staff time could an agent free up?

Set the sliders to match your support desk. We use a figure like this to open the first call, then check it against your own ticket data.

Staff hours freed each month160
Rough yearly saving$53,760

This assumes 60% of volume is automated. Before any work is signed, we test that share and the guardrails it needs against your real ticket history.

Production layer

What a live agent needs that a prototype lacks

Tool-use & MCP

Each action the agent can take is a typed tool or MCP server with its own permissions.

Retrieval (RAG)

Hybrid search, re-ranking and careful chunking, so replies point back to a source document.

Guardrails

Checks on what goes in and comes out, filters for personal information and prompt attacks, and a handoff rule.

Evals

A test suite runs on every prompt or model change, so regressions show up before customers see them.

Observability

Every request is traced, with dashboards for spend, response time and answer quality.

Model-agnostic

The provider sits behind an abstraction, so switching between Claude, GPT and open models is a config change.

Use cases

Jobs we hand to agents

Each of these begins as one narrow agent. It takes on more once the results hold up over a few weeks.

Support that resolves

Connected to your order, returns and policy data, the agent closes routine tickets on its own: tracking questions, refunds within policy, address changes. Unusual cases go to your team with the context already written up.

SDR & research agents

Before a rep picks up the phone, the agent has read the prospect's site, summarized what matters, drafted a first message and logged it in the CRM.

Knowledge (RAG) assistants

Staff ask a question and get an answer drawn from your manuals, wiki pages and past tickets, with links to the source. Useful for support desks, sales teams and onboarding.

Ops & back-office automation

Repetitive work that spans several systems, such as reconciling invoices, keying in forms or building weekly reports, done by an agent that logs each step it takes.

Voice agents

Phone agents that book appointments, qualify callers and answer common questions, using the same tools and safety rules as your chat agents.

AI-search visibility

Because our team also works on AI search, the help content behind your agent is written so ChatGPT, Perplexity and Google AI Overviews can quote it.

Common failures

Four problems we design around

Plenty of agents look good in a meeting and struggle with real customers. Here is what usually goes wrong and how our builds handle it.

It breaks on the second turn

A demo handles one question well. Real customers ask follow-ups, change their minds and refer back to earlier messages. We model conversation state and memory explicitly so a long thread still makes sense.

It makes things up

Left alone, a model will invent a refund rule or an order number. Our agents answer from retrieved documents, show where the answer came from, and pass uncertain cases to a person.

It can’t actually do anything

An agent that can only chat still leaves the work to your staff. We connect it to your systems through typed tools so it can issue the refund or book the appointment, limited to the permissions you set.

You can’t tell if it’s working

If nobody measures it, quality drifts unnoticed. Each deployment includes regression tests, a trace for every conversation and a dashboard showing what was resolved and what it cost.

AI Agents work at CreateITivity

Our commitments

When a business calls us about agents

A demo agent answers questions. A production agent acts on real systems, for real customers, with no one watching each step. These commitments are about closing that gap.

What the agent may do
A fixed list of tools, agreed before launch and enforced in code. It starts read-only. Refunds, cancellations and any other step that cannot be undone need explicit human approval.
What ships with every agent
Tests built from real cases in your own queue, guardrails, structured logs and a monitored route to a person. Without those it is still a prototype, and we will not call it finished.
When it gets something wrong
It hands off instead of guessing, the conversation is logged, and that case is added to the test set. Slippage shows up in testing before a customer runs into it.
Whose keys, whose data
Yours. Agents run on your accounts and systems and can switch between model providers, so you are not tied to one vendor or to us.
What it costs
A short scoping phase first, so the price in Canadian dollars reflects the systems the agent has to touch and the volume it will handle. How pricing works
The sequence

No customer sees it until it passes its tests

Building is the middle step. First we observe how your staff handle the work today. Last comes ongoing monitoring, because an agent that behaved in week six can slip by week twenty with no one noticing.

See the full process
  1. Week 1Shadow the people doing the work now
  2. Week 1–2Agree what it can and cannot touch
  3. Weeks 2–3Connected to your own documents
  4. Weeks 3–4Tests drawn from past conversations
  5. Weeks 4–5Runs silently beside your team
  6. Weeks 5–6Trial on a share of real traffic
  7. Weeks 6–8Full launch with a human handoff
  8. OngoingTests catch slippage before users do
Weigh it first

Be clear on what you are buying before any prompt is written

Plenty of tools sold as agents are chat windows with instructions attached. Start here to tell the two apart, decide who should build it, and learn the terms a scoping call will use.

  • ChatbotAI agent

    A chatbot replies. An agent does something in your systems, so it needs permission to act.

  • In-houseagency

    Whether this calls for a new hire, a fixed project, or nothing yet.

Terms to understand before you choose

Technical questions

What buyers ask before a build

Answers for CTOs, operations leads and owners weighing an agent project.

How is an AI agent different from a chatbot?
A chatbot replies. An agent also does things: it looks up records, updates systems and decides when a person should take over. We build agents, with the controls needed to leave them running.
Which AI models do you work with?
Claude, GPT and the stronger open-weight models. Each is better at different jobs, so we test against your workload for accuracy, cost and speed. The provider sits behind a layer we control, which keeps you free to switch later.
How do you keep it from inventing answers?
Answers come from retrieved documents and cite them. Guardrails check inputs and outputs, a confidence threshold triggers handoff to a person, and a test suite blocks any release that makes the agent worse.
How soon could we have one running?
Usually within a few weeks for a first workflow. We pick one narrow, high-volume task, launch it with guardrails on, check the numbers with you and only then widen its scope.
Who hosts the agent?
Your choice. We can deploy into your own cloud account, in a Canadian region if data residency matters to you, or operate it for you. Tracing, tests and cost monitoring come with both options.
Why would a managed IT provider build AI agents?
CreateITivity is also a development shop. The engineers who build your agent can make its help content readable to AI search engines, and they can implement technical SEO changes too. That saves you coordinating two vendors.
What does it cost to keep an agent running?
Ongoing cost is model usage plus hosting. We keep it down by sending simple turns to cheaper models and caching retrieval, and we show cost per resolved conversation on a dashboard so you can compare it with the staff time it saves.
Can it connect to the software we already use?
Yes. Connections to your CRM, helpdesk, order system or internal APIs are built as typed tools or MCP servers, so the agent works inside your existing setup.
Will our data be used to train AI models?
No. We use providers on terms that exclude training, keep retrieval data in systems you control, and can deploy inside your own cloud where privacy rules such as PIPEDA call for it.
What do we own once it is built?
All of it: source code, prompts, the evaluation suite and the infrastructure definitions. If we part ways, the agent keeps working and your team or another vendor can take it over.

Something we did not cover? Ask us directly

Start here

Tell us which repetitive work you want an agent to handle

Describe the task and roughly how often it comes up. We reply with whether an agent can do it safely, what systems it would need, and where a person should stay involved.

  • A reply within one business day from an engineer
  • A frank view on whether you need an agent or a simple script
  • No phone call needed to get it

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Free review · done by an engineer, not a script

Check whether this job suits an agent.

Describe the work and we tell you plainly whether an agent can handle it safely, what access it needs and where a person should stay involved.

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