Industrial AI, robotics conversion and adoption, Industry 4.0 automation and test automation, and the software those systems run inside: enterprise systems (ERP, HRMS, CRM, inventory and production management), web applications, AI agents and automation, and native mobile apps for iOS and Android.
A focused first release typically ships in 8–12 weeks. Multi-module enterprise platforms run 4–9 months, delivered module by module so you are using working software long before the final release rather than waiting on a single big-bang launch.
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More agents is a design choice, not a level you have reached.
The multi-agent diagram is the most persuasive slide in this field and, in ordinary business work, usually the wrong architecture. Every agent you add is another thing that can misread its brief, another handoff where meaning is lost, and another place a failure can start quietly. This is what each orchestration pattern actually is, what it costs you, and the narrower-than-advertised set of cases where more than one agent is genuinely the answer.
It looks like an org chart, and that is the problem.
The intuition behind multi-agent design is a good one — split the work the way you would split it across a team. What that intuition quietly assumes is everything a team does that these systems do not.
The appeal is immediate and it is not stupid. A hard job in a business is rarely done by one person: a researcher gathers, a specialist decides, a reviewer checks, a manager decides who does what next. Modelling software the same way feels like good engineering, because that is how work in the building is actually organised. The diagram draws itself — boxes with job titles, arrows between them — and it is legible to an executive in a way that a single prompt with eleven tools is not. Vendors reach for it for the same reason: an org chart is easier to sell than a well-specified function.
The word for wiring those boxes together is orchestration: the code that decides which agent runs, in what order, with what input, and what happens to its output. Orchestration is not a small addition to a system. It is a distributed system, with all of the usual properties — partial failure, retries, ordering, timeouts, state that two parties disagree about — except that each node is non-deterministic and expresses its errors in fluent English rather than as a stack trace.
Where the team intuition misleads is in what people carry between the boxes. A colleague who hands you a half-finished analysis also hands you the context that never made it onto the page: which numbers they were unsure about, what they already tried, that the client is difficult, that the deadline moved. They notice when the brief they were given makes no sense and they come back and ask. They can tell that the last three tasks they were handed were all subtly wrong and escalate. Agents do none of this. The handoff — everything one agent passes to the next — is exactly and only what got written into the message. Whatever the first agent knew and did not write down is gone, and the receiving agent has no way to know that anything is missing, so it proceeds confidently on a partial brief.
That gap has a price, and it is worth naming plainly. Coordination cost is what you pay for having more than one agent at all: the tokens spent restating context in each handoff, the latency of steps that now happen in sequence, the engineering time spent on retries and timeouts and partial results, and — the largest line, and the one nobody budgets for — the cost of understanding what happened when the output is wrong. In a single-agent system, a wrong answer has one transcript. In a five-agent system it has five, plus the orchestration decisions between them, and the error was often introduced three steps before the place it became visible.
None of this makes multi-agent architecture wrong. It makes it expensive, in a currency that does not appear on the invoice. The question worth asking is not whether several agents could do the job, but whether a specific, identifiable limit in the single-agent version is forcing you to split. There are such limits, they are real, and they are the subject of the rest of this page. What we argue against is not the pattern. It is reaching for the pattern before the limit has appeared.
A colleague hands over the doubt along with the draft. An agent hands over the message, and nothing else exists.
The patterns
Five ways to wire agents together, and what each one charges you.
These are the shapes you will meet, under the names you will meet them. Read the third line of each as the bill: what the pattern costs, or where it quietly stops working.
01
Supervisor, or delegator
A routing agent holds the goal and dispatches sub-tasks to specialist agents, then decides whether the result is sufficient or another round is needed
One agent plays manager. It reads the request, decides which specialist should handle the next step, sends it, reads what comes back, and decides whether the job is finished or something else needs doing. Everything routes through it, so it is the only part of the system that holds the whole goal. This is the pattern most people picture when they say multi-agent, and it is the one the orchestration frameworks make easiest to build, because the manager is just an agent whose tools happen to be other agents.
The supervisor is a single point of misjudgement with a compounding budget. It decides on every loop whether to stop, and an agent that is bad at knowing when it is done will keep delegating — we have seen straightforward requests spend real money going round a supervisor loop before hitting a limit. It also has to compress each specialist's output before passing it on, so the detail that mattered is frequently the detail dropped. And because it re-reads the accumulated state on every turn, its context grows with the length of the job, which is precisely when it can least afford to be distracted.
02
Pipeline
A fixed sequence of specialised steps: the output of stage one is the input of stage two, with a defined contract at each boundary
Extract, then classify, then draft, then check — always in that order, with each stage doing one narrow thing well. Nothing decides the route at runtime; the route is the design. Each boundary carries a defined shape of data, which means you can validate between stages, test a stage on its own, and replace one stage without touching the others. Some stages need not be a language model at all: a database lookup, a rules engine or a regular function is often the right occupant of a slot.
It cannot adapt. Anything the sequence did not anticipate either falls through or gets forced into a stage that was not built for it, and the only fix is changing the design rather than the input. Errors also travel forwards and grow: a misreading in stage one is treated as established fact by stages two, three and four, each of which builds on it confidently. If you fix that by validating hard between every stage, you have rebuilt a conventional integration — which may well be the right answer, but be honest that this is what happened.
03
Parallel fan-out with a reducer
The same task is dispatched across many independent items at once, and a reducer collects the results into one output
Fan-out means sending the work out in parallel rather than one after another: forty documents to summarise, one agent call each, all at the same time. A reducer is the step that puts the results back together — merging, ranking, deduplicating or summarising the pile into a single answer. This is the pattern that most reliably pays for itself, because the split is along the data rather than along judgement, and the branches genuinely do not need to talk to each other. The win is wall-clock time: forty things in the time of one.
It only works when the items really are independent, and people apply it to things that are not — forty sections of one contract are not forty separate contracts, and summarising each alone loses every cross-reference. The reducer is also where the difficulty concentrates and where it is least examined: it has to resolve branches that contradict each other, and if the reducer is itself a language model reading forty results, you have just built a long-context problem to solve a long-context problem. Cost is now parallel too, so a bug bills forty times before anyone sees the output.
04
Debate, or critic
A second agent reviews, challenges or scores the first agent's output against explicit criteria before it is accepted
One agent produces, another checks. In the strict form the critic holds a written rubric — the specific things a good answer must have — and returns a pass, a fail or a list of defects, and the producer revises. This works when the criteria can be stated in advance and checking is genuinely easier than producing, which is a real and common asymmetry: spotting that a citation does not support the claim is easier than writing the correct claim.
Both agents usually share a model, and so share its blind spots — a critic will happily approve the exact error it would have made itself, which is worse than no check because it manufactures confidence. Without a written rubric, critics converge on agreeable, non-specific feedback and the loop adds latency and cost for nothing. Where the criteria can be stated precisely, they can very often be checked by ordinary code — schema validation, a lookup against the source record, a numerical reconciliation — which is faster, cheaper and does not have opinions. Reach for a second model only for the judgements code cannot make.
05
Shared-memory swarm
Several agents work concurrently against a common scratchpad or state store, reading and writing as they go, with no fixed sequence
No manager and no fixed order. Agents run against one shared workspace — a document, a task list, a state store — picking up what looks unfinished, writing back what they have done, and reacting to each other's writes. It is the most autonomous-looking arrangement and the one that produces the most impressive demonstrations, because behaviour emerges that nobody wrote down.
Behaviour that nobody wrote down is behaviour that nobody can predict, reproduce or sign off. You inherit every hazard of concurrent writing — two agents editing the same state on stale reads, work done twice, work dropped because each assumed the other had it — with none of the usual defences, because there are no transactions and no locks in a natural-language scratchpad. The same input will not produce the same run twice, which makes testing, incident review and any regulated audit trail extremely difficult. In our experience this belongs in research, not in a process a business depends on this quarter.
Worth the cost, how often
Ranked by how often the coordination cost is repaid.
The same five patterns, ordered by how frequently they turn out to be worth their coordination cost in ordinary business work — the document, ticket, quote and record handling that most systems are actually made of.
Read the order as an argument, not as a measurement. This is a ranking drawn from what these patterns cost to build and run against how often they repay it in ordinary business work — it is not a benchmark, a survey or a success rate, and no number was measured to produce it. A pattern low in this list is not a bad pattern; it is one that needs a more specific reason before you reach for it. Your own ranking should differ where your work differs.
The honest test
Some jobs need more than one agent. Most need one agent and better tools.
The useful question is not whether several agents could do the work. It is whether a specific limit in the single-agent version is forcing the split. If you cannot name the limit, you are buying coordination cost with nothing to spend it on.
Genuinely needs more than one agent
The work is many independent items and the time it takes matters — forty documents summarised at once is a real win, and the branches never need to talk to each other
Two parts of the job have genuinely different permissions, and one must never be able to reach what the other can — a separate agent with a separate, smaller set of tools is a boundary you can actually enforce and audit
The single agent has too many tools to choose between reliably, the failures are clearly failures of selection rather than of reasoning, and narrowing the choice is what fixes them
One stage needs a different model from another for a defensible reason — a cheap fast model for classifying every inbound item, an expensive one only for the few that pass the filter
Producing and checking are asymmetric, the checking criteria are written down, and the check needs judgement that ordinary validation code cannot express
One agent with better tools
The reason for splitting is that the prompt got long and unwieldy — that is a context engineering problem, and dividing it across agents hides the mess rather than removing it
The steps always run in the same order with no decision between them, which is a function call sequence with a language model inside it, not an architecture
The specialists would all share the same model and the same tools, differing only in their instructions — you have one agent wearing five hats and paying five times to change them
Nobody can yet say what a correct final answer looks like; adding agents multiplies an unmeasured system rather than fixing it, and you will not be able to tell which agent made it worse
The split exists because the architecture diagram looks more serious with more boxes on it, or because a framework made the second agent easy to add
Asked often
The questions that come up once someone has seen the demo.
Doesn't an orchestration framework solve all of this?
It solves the part that was never the hard part. The orchestration frameworks give you message passing, state, retries, tracing and a way to define handoffs, and that is genuine engineering work you would otherwise write yourself — worth using once you have decided to build the thing. What no framework supplies is the judgement about whether an agent should be able to see a customer's bank details, what a correct answer looks like for your task, or which of five agents caused today's wrong output. A framework makes the second agent cheap to add, which is convenient when you need one and dangerous when you do not.
How do failures compound across agents?
Multiplicatively, and the arithmetic is unforgiving. If each of five steps is right ninety-five per cent of the time and each depends on the last, the end-to-end result is right about seventy-seven per cent of the time — nobody built a seventy-seven per cent system on purpose. Worse, the errors are not random noise that averages out. A misreading in step one is passed on as established fact, and every later agent reasons faithfully from it, producing an answer that is coherent, well-argued and wrong. This is the practical case for validating at each boundary, and for keeping the number of boundaries small.
How do we debug something like this?
You cannot, unless you built for it from the first day. What is needed is a single trace identifier that follows one request through every agent, the full input and output of each hop recorded as it actually was rather than summarised, the orchestration decisions logged with their reasons, and a way to replay one hop in isolation with the exact input it received. Without that you are reading five transcripts and guessing. Budget for this at the start: retrofitting observability onto a multi-agent system that is already in production is materially harder than building it in, and until it exists nobody can answer the only question that matters, which is which agent introduced the error.
What does it actually cost?
Three lines, and the third dominates. Tokens rise faster than the number of agents, because context is restated at every handoff and a supervisor re-reads accumulated state on every turn — a five-agent system is comfortably more than five times a single call. Latency rises with anything sequential, which is what turns a four-second answer into a forty-second one and changes what the tool can be used for. The largest line is people: the engineering to make handoffs reliable, the observability to make failures legible, and the time spent every week understanding what happened. Put a real number against all three before comparing them to one agent with a well-chosen set of tools.
Can we start with one agent and split later?
Yes, and that is the order we recommend. Build one well-bounded agent, instrument it properly, and let it tell you where it breaks — the split points reveal themselves as specific limits: this tool set is too large to choose from reliably, this stage needs permissions the rest must not have, this takes ninety seconds because it runs in sequence. Splitting on evidence produces a boundary in the right place. Splitting on a diagram produces boundaries in the wrong places, and moving one afterwards is harder than making it, because by then the whole system's assumptions are built on it.
In practice
Start with one. Split when a limit forces it.
The position we hold, and will argue for, is that one well-bounded agent with a carefully chosen set of tools beats five loosely-bounded ones in most business settings. It is easier to evaluate, because there is one output to judge. It is easier to debug, because there is one transcript. It is cheaper to run and far cheaper to change. And when it does break, the reason is legible to the person who has to fix it on a Tuesday afternoon.
Splitting is a decision to be made against evidence rather than against a diagram. Name the limit first: the tool set the agent cannot reliably choose within, the permission boundary that has to be enforced rather than instructed, the work that is genuinely parallel and genuinely slow. Then split on that one limit, keep the boundary narrow, and instrument the handoff before you rely on it. Everything else is coordination cost with nothing bought.
If you are being shown a multi-agent architecture and are not sure whether it is the right shape for the work, that is a short and worthwhile conversation to have with someone who has no reason to sell you the extra boxes.
OpenAI
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