Your single prompt gives a confident answer that's wrong half the time.
Multi-agent LLM development: pipelines where disagreement between agents is the feature, resolved explicitly at synthesis and checked against a scored evaluation set.
Three signs a single prompt has hit its ceiling
- One giant prompt tries to research, analyse and write in a single pass, and quietly drops steps under pressure.
- Nobody can say why an answer changed between two runs, or catch it when it gets worse.
- A prompt change ships on a hunch, with no scored set to check it against before or after.
How a multi-agent system gets built
Split the task by role, not by prompt length
Research, analysis, risk and writing become separate agents with narrow responsibilities — each easier to evaluate and debug than one prompt doing everything.
Design the synthesis step to expect disagreement
Conflicting findings between agents are surfaced and resolved explicitly at synthesis, not silently averaged away.
Build a scored evaluation set before shipping changes
Any prompt change that touches production runs against the same scored set first, so a regression is caught before a user sees it.
Track cost per run from day one
Multiple agents means multiple calls — cost is measured per run, not discovered on the first invoice.
Guard against agents converging on each other's language
A known failure mode where agents start echoing each other instead of contributing independent perspective — checked for explicitly, not assumed away.
Four agents, one recommendation, conflicts resolved on purpose
A multi-agent business idea analyst where four specialised agents — research, analysis, risk and writing — reconcile conflicting findings into a single recommendation. The hard parts were conflict resolution at synthesis, controlling cost per run, and stopping the agents from converging on each other's language.
Concrete deliverables
A graph definition, not a black box
The agent graph is documented and inspectable, not a single unexplainable prompt chain.
A scored evaluation set
A reusable set to check every future prompt or model change against, before it ships.
An explicit conflict-resolution step
A synthesis stage that names and resolves disagreement instead of hiding it.
A measured cost per run
Token and call cost tracked per execution, so scaling cost is predictable.
The two questions every team asks
"Isn't more agents just more places for things to go wrong?"
Fewer, actually — a narrow agent with one job is easier to evaluate and fix than a single prompt doing five things at once. The trade-off is real, though: more agents means more calls and more synthesis logic, which is why the evaluation set and conflict-resolution step aren't optional extras.
"What happens to our LLM bill?"
It goes up per run and needs to be tracked from the start, not discovered later. Cost per run is measured during development, and cheaper models are used for narrower sub-tasks where a smaller model performs just as well as a frontier one.
A multi-agent build starts at $6,000
The Build tier covers a full agent or LLM system end to end, deployed, across 4–8 weeks. Full tiers and payment terms are on the pricing page.