AI Agents & Automation · Multi-Agent Systems
Specialist agents that hand off like a real team
We design systems where several focused agents each play one role — researcher, drafter, reviewer, decider — coordinated by an orchestrator that routes the work between them and keeps a shared state they all read from.
Researcher
gathers the evidence
Drafter
produces the output
Reviewer
critiques the work
Decider
signs off or sends it back
The approach
Multi-agent systems built as a coordinated cast of specialists
Some problems are too broad for one agent to do well. We break them into roles — one agent gathers evidence, another drafts, a third critiques, a fourth decides — and design the orchestration that hands work between them. A shared state carries context across the handoffs, and a reviewer role checks the others' output before anything is finalized.
Systems where specialist agents each own a role and hand off to one another under an orchestrator — research, draft, review, decide — with shared state between them.
Roles that each do one thing well
Splitting a broad problem into a researcher, a drafter, and a reviewer lets each agent stay focused and sharper than one agent stretched across the whole job.
Coordinated handoffs
An orchestrator routes work between the agents and carries context in a shared state, so a handoff passes on what the next role needs instead of starting cold.
A built-in critic
A dedicated reviewer role checks another agent's output before it's accepted, catching weak reasoning inside the system rather than after it ships.
The cast
Four roles, each doing one thing well
Role decomposition
We break the problem into specialist roles — research, drafting, review, decision — each an agent with its own instructions and tools.
- Role definition
- Per-agent tooling
- Focused prompts
Orchestration & handoffs
An orchestrator that decides which agent runs next and passes the work along, so the cast operates in a coordinated sequence rather than in parallel chaos.
- Orchestrator logic
- Handoff routing
- Turn control
Shared state & context
A shared memory the agents read from and write to, so context survives each handoff instead of being re-derived from scratch.
- Shared memory
- Context passing
- Intermediate results
Reviewer & control roles
A critic agent that evaluates output and a controller that stops loops, caps cost, and ends the run when the goal is met.
- Critic agent
- Loop limits
- Termination rules
Engineering the ensemble
Where multi-agent systems break, and what we build against each
Getting a few agents to produce something once is quick. Getting them to hand off reliably, share what they know, stay inside their permissions and stop when they should is the actual work.
These are the seven places a cast goes wrong in production. Each one is designed for before the first real run, instead of patched after it.
RoutingWork that goes to the wrong agent, or nowhere
When several agents can act, something has to decide who acts next. Left to the agents themselves, work bounces between roles or stalls with each waiting on another. We build an orchestrator that owns the sequence: it reads the shared state, picks the next role, and hands the work over explicitly.
- One orchestrator owns which agent runs next
- Explicit handoffs instead of agents messaging freely
- Routing rules you can read, test and change
ContextHandoffs that lose what the last agent learned
An agent that starts cold re-derives what the previous one already found, and gets it slightly different. We give the cast a shared state it reads from and writes to, with a defined shape for what each role passes on, so the drafter works from the researcher's evidence rather than its own guess.
- Shared state with a defined structure per handoff
- Intermediate results kept, never regenerated
- Only the context each role needs, instead of the whole transcript
ToolsAgents with more access than their role needs
A researcher that can also send email is a risk nobody meant to create. Each agent gets the tools and data its role requires and nothing else, so a mistake in one role cannot reach systems that belong to another.
- Per-role tool and data permissions
- Read-only access wherever writing isn't the job
- Actions in your systems gated to the roles that own them
ReviewWeak output passed along as if it were finished
Without a critic, each agent trusts the one before it and errors compound down the chain. We put a reviewer role inside the system that checks work against defined criteria and sends it back with reasons, so a weak step is caught between agents instead of in the final result.
- A reviewer role with written acceptance criteria
- Rejected work returned with the reason attached
- A cap on revision rounds before it escalates
BoundsRuns that loop, stall or run up cost
Agents can keep talking to each other long after the useful work is done. A controller sets the limits before the first run: how many turns, how much spend, how long, and what counts as finished, and it ends the run cleanly when any of them is reached.
- Turn, time and spend limits on every run
- Termination rules for when the goal is met
- A clean stop with the partial result kept
VisibilityA result nobody can explain afterwards
When a cast produces something wrong, you need to know which role went wrong and why. Every handoff, tool call and review decision is logged against the run, so a bad result can be traced to the step that caused it and that step fixed.
- A trace of every handoff and tool call per run
- Review decisions recorded with their reasons
- Runs you can replay when tuning a role
PeopleDecisions that should have been a person's
Some steps should never be fully automatic: an approval, a customer-facing message, a change to a record that matters. We mark those points in the orchestration so the run pauses, a person decides with the context in front of them, and the cast carries on from there.
- Approval points placed in the orchestration
- The case handed to a person with its context
- The run resumes from the decision, not from scratch
The score
How the ensemble comes together
Cast the roles
Decide which specialist agents the problem needs and what each one is responsible for producing.
Design the handoffs
Define the orchestration — who runs when, and what each agent passes to the next through shared state.
Add the critic
Introduce a reviewer role and the control logic that caps loops, cost, and when the system stops.
Tune the ensemble
Run the system on real inputs and adjust roles, handoffs, and review criteria until the cast coordinates cleanly.
Getting started
Three steps from a broad problem to a running cast
Check the problem splits
We look at the job with you and decide honestly whether it divides into distinct roles. If one agent or a plain workflow would do it, we say so and scope that instead.
Prove the cast on real inputs
We build the roles, handoffs and reviewer against a sample of your actual work, so you see how the ensemble handles your cases before committing to production.
Bound it, connect it, run it
We add the limits, logging and approval points, connect the agents to your systems with the access each role needs, and stay on to tune it as real runs come in.
Why it matters
What changes when the work is split into roles
These are the effects, not the feature list. Roles, handoffs, and a reviewer are what the system is made of. Focused work, checked output, and results you can explain are what your team actually notices.
Focus
Each step done by an agent built for it
A researcher, a drafter and a reviewer each hold one instruction set and one set of tools, instead of a single agent trying to do all three in one pass.
Narrow roles are easier to get right, and easier to improve one at a time.
Quality
Work checked before it moves on
The reviewer role reads another agent's output against criteria you agree, and sends it back when it falls short.
Mistakes are caught inside the run, long before the person who receives the result.
Control
Limits set before anything runs
Turn, time and spend limits, termination rules and per-role permissions are part of the design from the first run.
The system stops when it should, and no role can reach further than its job.
Traceability
Every result explained step by step
Each handoff, tool call and review decision is logged against the run it belongs to.
When something is wrong you can see which role caused it, and fix that role.
Change
One role improved without rebuilding the rest
Because the roles are separate, a better drafter or a stricter reviewer can be swapped in without touching the others.
The system gets better in small, testable steps as you learn what your cases need.
Judgement
People kept where their judgment matters
Approval points in the orchestration hand specific decisions to a person, with the case in front of them.
The cast does the gathering, drafting and checking; people make the calls that carry weight.
What you get
A working system, not a slide deck
Every engagement ends with the orchestration running, documented, and bounded, ready to hand over to your team.
Multi-agent system
The orchestrated set of specialist agents with their roles, handoffs, and shared state wired together.
Orchestration design
Documentation of the roles, the routing logic, and how context passes between agents.
Review & control rules
The critic role, loop limits, and termination conditions that keep the system bounded.
Why Flaidex
Why teams build multi-agent systems with Flaidex
One senior team scopes the problem, designs the orchestration, builds the software around it and stays on to tune it, so the cast you run is the one that was designed.
01We tell you when one agent is enough
Multi-agent architecture adds coordination overhead, and plenty of problems don't need it. We scope the job before recommending a cast, and point you to a single agent or a workflow when that is the better fit.
- An honest fit check before any build
- Simpler options considered first
- A recommendation you can take elsewhere
“The right number of agents is the smallest one that does the job well.”
The first conversation is about the problem, before any architecture: what the job involves, where it breaks into roles, and whether those roles gain anything from handing off.
Role mapping
Which distinct jobs the problem actually contains.
Fit check
Whether handoffs and review add enough to justify them.
Alternatives
A single agent or workflow, weighed alongside.
Scope
The smallest cast that can be proven on real inputs.
02Orchestration treated as the engineering
Wiring a few agents together is quick. Making them hand off reliably, share context cleanly and stop when they should is where the effort goes, and it is where we spend ours.
- Orchestrator logic you can read and test
- Structured shared state between roles
- Termination and limits designed in
“The agents are the easy part; the handoffs are the product.”
We design the sequence, the shape of each handoff and the control rules first, then write the agents to fit them.
Routing
An orchestrator that owns who runs next.
Shared state
A defined record each role reads and writes.
Controller
Turn, time and spend limits on every run.
Reviewer
A critic role with written acceptance criteria.
03Connected to the systems you already run
A cast is only useful if it can read your data and act in your tools. We connect each role to the CRMs, databases, documents and APIs it needs, with access limited to what that role's job requires.
- Integrations with your existing stack
- Per-role permissions on tools and data
- Approval points before consequential actions
“Each agent gets the access its role needs, and nothing more.”
We map which role touches which system, what it may read, and what it may change, before connecting anything.
Integrations
CRMs, databases, document stores and APIs.
Least access
Read-only wherever writing isn't the job.
Human gates
People approve the steps that carry weight.
Audit trail
Every action recorded against its run.
04Built by a team that ships and maintains software
The same team designs the interfaces, writes the services around the agents, tests them, and deploys them, so the system arrives as maintainable software instead of a notebook that worked once.
- Review and admin screens where people need them
- Tests on routing, handoffs and limits
- Deployment and monitoring set up with it
“Production agents are software first, and should be built like it.”
The cast runs inside a normal, tested application with logging, configuration and deployment your team can own.
Interfaces
Screens for review, approvals and run history.
Testing
Replayable runs to check changes before release.
Deployment
Shipped with configuration and environments.
Ownership
Code and documentation your team can extend.
05We stay on as real runs come in
Real inputs find cases a prototype never saw. After launch we watch the traces, tighten the reviewer's criteria, adjust roles and handoffs, and add new roles when the job grows.
- Tuning from real run traces
- Role and review criteria refined
- New roles added as the work expands
“A cast gets better by being watched, one role at a time.”
Ongoing support covers role and prompt changes, model updates, integration changes and the limits that keep runs bounded.
Trace review
Where runs went wrong, and which role caused it.
Role tuning
Instructions and tools adjusted per agent.
Model updates
Changes tested against replayed runs first.
Growth
New roles added without rebuilding the rest.
Work that tends to split into roles
Questions
What clients ask
Not sure a multi-agent system is right for your problem? Ask us, and we'll tell you honestly.
Why use several agents instead of one capable agent?
Because a single agent asked to research, write, and critique in one pass tends to do all three adequately and none of them well. Splitting the work into roles lets each agent hold a focused instruction set and its own tools, and it lets a reviewer role check another's output. The gain is separation of concerns — the same reason you'd have a writer and an editor rather than one person doing both in a single draft.
How do the agents actually coordinate?
An orchestrator governs the sequence — it decides which agent runs next and hands the work along, rather than letting them all talk at once. Context travels through a shared state that each agent reads from and writes to, so when the researcher finishes, the drafter picks up what it found instead of starting cold. The orchestration is the real engineering; the individual agents are the easy part.
What stops the agents from looping forever or running up cost?
A controller with explicit limits. We set a cap on how many turns the system can take, termination rules for when the goal is met, and budgets that stop a run before it spirals. Multi-agent systems can chatter among themselves indefinitely without these bounds, so the control logic that ends a run cleanly is designed in from the start, not bolted on after a runaway bill.
How do you keep quality high across the handoffs?
With a dedicated reviewer role. Rather than trusting each agent's output blindly, a critic agent evaluates the work against defined criteria before it's accepted, sending it back for another pass if it falls short. That review step lives inside the system, so weak reasoning is caught between agents rather than surfacing in the final result a user sees.
Is a multi-agent system overkill for our problem?
Often, yes — and we'll tell you when it is. If your job is a single capability or a linear process, one agent or a workflow is simpler and cheaper to run. Multi-agent architecture earns its complexity only when the problem genuinely splits into distinct roles that benefit from handoffs and review. We scope that honestly before recommending it, because the coordination overhead is real.
Have a problem too broad for a single agent?
Tell us the job — we'll cast the specialist roles, design the handoffs, and build the orchestration that coordinates them.
