Multi-agent systems in Abbotsford
Several specialists, one coordinator, and a clear record of who did what.
Multi-agent systems split a large job between specialist AI agents, with a coordinator passing work between them. We tell you honestly when one agent is enough, and when dividing the job helps, then build the handoffs, limits and tracing that keep it manageable.
Free audit, no obligation · In-house team since 2015 · Abbotsford studio
Honest questions about multi-agent systems before you commit
More agents can mean more cost and more ways to fail. These are the doubts we would raise ourselves, each followed by our answer and the change you would feel.
- 01
“I suspect several agents is more than this job needs.”
WHAT WE DOWe begin with a single agent and measure where it struggles. We only split the job when one agent's instructions grow tangled, its tools conflict or one task needs a different level of caution.WHAT CHANGESYou add complexity only when it earns its place, and sometimes one agent is the honest answer. - 02
“With several agents working, how would I know who did what?”
WHAT WE DOEach agent writes to a shared trace with its task, inputs, tool calls and result, stamped with the time. We add a simple view that follows one request from start to finish across all of them.WHAT CHANGESWhen something looks wrong, you can trace it to the step and the agent responsible. - 03
“I'm worried one confused agent will mislead all the others.”
WHAT WE DOAgents pass structured handoffs, not free-form chat, and a checking step compares important results with your records before the next agent relies on them. A failure stops that branch and alerts a person.WHAT CHANGESA bad result is more likely to be stopped early than to spread down the line. - 04
“Every extra agent means another bill for model usage.”
WHAT WE DOWe estimate usage per request before building, give each agent a spending ceiling and choose smaller, cheaper models for simple steps. A dashboard shows what each agent used so cost surprises show up early.WHAT CHANGESRunning costs are visible and capped, so growth does not catch you off guard.
Add agents only when one is not enough.
A multi-agent system is a small team of AI programs, each with a narrow job, plus a coordinator that hands work from one to the next. Think of an intake clerk, a researcher and a reviewer who never meet but pass notes. The design can make complicated work easier to test, yet every extra agent adds cost, delay and another place for an error to hide.
That is why we start with one agent and split only for a reason: instructions that grow tangled, tools that should be kept apart, or steps that need different levels of caution. We plan the handoffs, the shared notes and the spending limits first, and we build a trace so you can follow any request through every agent it touched.
- One AI assistant's instructions have become tangled
- Parts of a job need very different permissions
- You cannot say why a long automated task failed
Where multi-agent systems help, and where they do not
How things look once the marketing is done right.
- 01
Specialists for separate jobs
One agent reads incoming requests, another checks stock or records, a third drafts the reply. A narrow role is simpler to test, and simpler to repair, than one agent juggling every task.
- 02
Different caution for each step
The agent that only reads files can have broad access, while the one that sends messages is held to strict limits and approvals.
- 03
Work done side by side
Independent tasks can run at the same moment, such as checking a document and looking up a customer, which shortens waiting when volume rises.
- 04
A trace you can follow
A full record of handoffs and decisions lets you find which step went wrong, fix that one agent and leave the rest alone.
What a multi-agent systems project includes
- 01Single-agent baseline first
- 02Role and coordinator design
- 03Handoff rules between agents
- 04Shared state and records
- 05Spending ceiling per agent
- 06Failure and retry handling
- 07Step-by-step trace viewer
- 08Testing on real workloads
The tools in our toolkit.
- LangGraph
- CrewAI
- OpenAI API
- Claude
- Redis
- Python
- 01Fixed-scope projectScope, schedule and price set up front. Suits MVPs, websites and builds with clear requirements.
- 02Dedicated teamDevelopers, designers and testers join your team on a monthly arrangement.
- 03Support retainerRegular upkeep, upgrades and improvements, with response times agreed in advance.
Our Multi-agent systems process, step by step
- 01
Discover
We sit down with you to learn your goals, customers and limits, then hand back a written scope and estimate.
- 02
Design
Screen flows, wireframes and clickable mockups get tested with real users before any code is written.
- 03
Build & test
Two-week build cycles that end in a working demo, backed by code reviews and automated tests.
- 04
Launch & support
A phased launch, live monitoring, team training and support that continues as you grow.
What clients say
“Our Google Ads campaigns were burning through budget with little to show for it. All In One Marketing Pro stepped in and completely revamped our strategy, targeting the right audience and optimizing our ads for conversions and our website also google map. Within three months, our cost per lead dropped by 65%, and our ROI improved significantly. They solved a problem we thought was unsolvable! Thank you so much Lovi and all in one marketing team”
Alissa Mandzuk-PowerClient review “We had no real strategy for social media before, but after working with All In One Marketing Pro, our engagement skyrocketed! Our Instagram and Facebook pages grew by over 150%, and we’re now getting consistent leads directly from social media. Their content is top-notch, and they truly understand how to turn followers into customers.”
N KaurClient review “All in One Marketing has played a crucial role in the growth of my cardiology practice. Their targeted strategies have greatly enhanced patient engagement through social media, and by the second month of running Google Ads, we began seeing a steady influx of new patients. They are highly professional and results-driven! Thank you”
Emma NClient review
Each review is quoted as the client wrote it. Results depend on your market, your budget and where you start.
It is a group of AI agents that each handle one part of a job, with a coordinating agent or program passing work between them. Picture a front desk, a research assistant and a proofreader sharing a task. The idea is to keep each agent simple and easy to test, though the whole arrangement takes more effort to operate than one agent alone.
One agent is enough for most small jobs, and it is cheaper and easier to follow. Splitting helps when instructions conflict, when steps need different permissions, or when independent tasks can run side by side. If nobody can name the reason for a second agent, we advise against adding it, because every extra agent adds cost and delay.
Often, yes. If the steps are known and the inputs are tidy, traditional software or a plain workflow tool does the job more cheaply and more predictably than any number of agents. Agents earn their place where input is messy language or cases vary widely. We map your process first, keep the fixed parts in ordinary software and give agents only the judgement-heavy parts.
Each agent gets a spending ceiling, a retry limit and a rule for what to do on failure, usually stop and alert a person. Simple steps use cheaper models. A trace records every handoff, so a problem can be followed back to one step. Costs still depend on usage, so we estimate them with you before building.