Agents by Studio Ploski · Alberta, Canada

Automation first.
Agents only when the path breaks.

We build the fixed code and automations that run the known work, then hand off to agentic reasoning when judgment, messy input, or an exception appears. That is how output stays consistent — and how the bill stays predictable.

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The practice

What we build.

Most of a durable system is automation and code. Agents cover the stretch the rules cannot. We design both, and the handoff between them.

Automations
Fixed paths in code

Intake, validation, routing, writes, notifications, and the audit trail. If the step is known, it should not pay for a model call.

Handoff
When the path breaks

Messy documents, missing fields, conflicting rules, or a case that needs judgment. That is the only place an agent should spend tokens.

Agents
Reasoning on a leash

Read the exception, call tools, draft the file, and stop for a person when the decision binds the business. Then return control to the workflow.

The method

Set the path. Automate it. Hand off when required.

Agentic engineering stays solvent when most cases never reach the model. Consistency comes from code. Coverage comes from the agent.

01 · Code
Encode the known job

Statuses, required fields, system writes, and stop conditions live in software. Same input, same output, every time.

02 · Automation
Run the path without a person

The workflow moves the file: collect, check, route, notify. Operators only see work that actually needs them.

03 · Agent
Think only on the exception

When the path cannot finish, the agent reads context, proposes the next action, and hands back a reviewable result.

Where we work

One shipped practice. Five illustrative maps.

Financial services is work we have built. The other industries show how the same automation-first pattern would apply — Imagine cards, not case studies.

Financial services · Built
Onboarding that does not stall the file

We have built this for regulated banking onboarding: automation collects and checks the known KYC pack; an agent steps in on gaps, conflicts, and odd documents so an officer reviews a complete file.

Also imagine
  • Ongoing due-diligence packs assembled from the last review, not from a blank page
  • Exception queues that explain why a file stopped, and what is still missing
Aerospace & defence · Imagine
Answers with the source attached

Illustrative: technical publications, configuration rules, and supplier packets are large and versioned. An agent earns its keep when it retrieves the right revision and leaves a trail.

Imagine
  • A change-package assistant that drafts the impact notes and flags missing approvals
  • A quality packet reviewer that checks a submission against the required evidence list
Manufacturing · Imagine
The line does not wait for a ticket

Illustrative: most plant pain is not “more dashboards.” It is the exception that sits in someone’s inbox while the work order ages.

Imagine
  • A work-order exception agent that gathers MES, inventory, and last-known cause before it pages a supervisor
  • An MRO lookup that turns a part description into a stocked alternative, with the substitution rule cited
Pharmaceutical · Imagine
Procedure first. Draft second.

Illustrative: useful agents in this industry cite the SOP, stay inside the validated process, and never quietly “improve” a controlled record.

Imagine
  • An SOP copilot that answers with section and effective date, not a paraphrase
  • A deviation first-draft pack: timeline, affected lots, and the questions QA still has to answer
Hospitality · Imagine
The request should not bounce between desks

Illustrative: guests and staff generate the same handful of exceptions every day. An agent that routes, drafts, and closes those loops is worth more than a lobby chatbot.

Imagine
  • A guest-request agent that books, escalates, or writes the front-desk note in the property system
  • A pre-shift brief built from arrivals, VIP flags, and yesterday’s unresolved issues
Retail · Imagine
Exceptions at the edge of the store

Illustrative: returns, vendor files, and inventory mismatches eat hours that should be on the floor. Agents help when they finish the case, not when they summarize it.

Imagine
  • A returns exception agent that applies policy, asks for the one missing photo, and prepares the refund decision
  • A vendor onboarding file that checks documents once, then tells merchandising what is still outstanding

How we work

Five stages. One owner through production.

We map the deterministic path first. The agent is scoped to what that path cannot finish.

1
Find the job

One workflow that already costs time, risk, or backlog — and the steps that already have rules.

2
Split the path

What stays in code and automation. What may call an agent. Where a person must still decide.

3
Build the spine

Automations against live systems first. The agent is wired only at the handoff points.

4
Pilot with operators

Measure how many cases finish without the model, how many need the agent, and what still needs a person.

5
Run and hand over

Logging, ownership, and a way to tighten the rules so fewer cases hit the agent next month.

How we decide

What we will not skip.

A person on consequential decisions

Approvals, safety, credit, quality, and anything that binds the business stay human-gated. The agent prepares the file. It does not quietly close it.

A trail someone can audit

What the agent saw, which tool it called, and why it stopped. Useful to operations first, then to compliance.

Your stack, not a new island

We integrate with the systems you already run. A second portal that nobody opens is not a result.

Software we can stand behind

Studio Ploski ships its own products as well as client work. We treat agents as production software — scoped, tested, and owned after go-live. Proof on the apps page: SiteToMD, Team Treasurer, and GordAI.

Questions we hear first

Straight answers.

Why not let an agent run the whole workflow?

Because the known path should be free, fast, and identical every time. Models drift, cost scales with volume, and auditors want a rule they can point at. We put that path in code and automation. The agent is called when the path cannot finish — messy input, a conflict, or a judgment call.

How is an agent different from a chatbot?

A chatbot answers. An agent takes steps on an exception: it reads a file, calls a system, writes a draft, opens a ticket, or stops and asks a person. The automation around it is what makes those steps safe to run every day.

Where does the data go?

We design to your constraints. That can mean vendor models with a data-processing agreement, Canadian or private-cloud hosting, or keeping retrieval and logs inside your tenancy. We settle residency and retention before a pilot, not after a legal review finds them.

How long does a first engagement take?

A focused pilot on one workflow is typically four to eight weeks from kickoff to operators using it, if the source systems are reachable and a process owner is available. Broader programmes follow after the first job is real.

Who owns what we build?

You own the workflow, prompts, evaluations, and integrations we create for you. We do not lock the work behind a studio-only platform. Shared methods stay with us; your process stays with you.

What do you need from us to start?

A named process owner, access to a sample of real cases (redacted if required), and the systems the work already touches. We do not need a strategy offsite first.

Project inquiry

Tell us the workflow.

Describe the job as it runs today — for example, subject “Workflow: KYC exception queue”. This form opens a message to luke@ploski.ca — no account, no waiting room.

Engagement details

Opens your email client with the details filled in.