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Multi-Agent AI Systems Development
Multi-agent AI systems for workflows one agent can't handle.
Complex workflows that require multiple types of intelligence, research and synthesis, decision-making and action, quality review and revision, can't be reliably handled by a single AI agent. Multi-agent systems assign specialised agents to each step: one agent researches, another decides, another executes, another validates.
We build multi-agent AI systems that decompose complex tasks into agent-specific subtasks, coordinate the handoffs between agents, and produce reliable outputs from workflows too complex for a single model or prompt to handle.
Multi-agent architectures built for your specific multi-step workflow and task decomposition
Orchestrator and worker agent designs with defined handoffs, tool use, and error recovery
Works with OpenAI GPT-4o, Claude, Gemini, Llama, and open-source models, or multi-model combinations
Full source code ownership, the agent infrastructure runs in your environment, not a third-party platform
Recent outcomes
Multi-agent AI · Document processing pipeline
20,000+ daily transactions
Built an orchestrator-worker system that extracted, validated, and routed 20,000+ transactions a day.
AI agent · Healthcare workflows
20% faster clinical decisions
Built a multi-agent RPM system with data collection, clinical decision, and alert agents. 150+ patients onboarded in 12 weeks.
4.9
on ClutchThe problem
Sound familiar?
Single AI agent failing on complex multi-step tasks that require different types of reasoning at each step?
Workflow too complex for one LLM to handle reliably, needs to be broken into specialised subtasks?
Short answer
RaftLabs builds multi-agent AI systems for businesses across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia where a single AI agent fails on complex tasks. We design orchestrator-worker architectures with defined handoffs for document processing, research, and decisions. Projects deliver in 6-14 weeks at a fixed cost from $20,000.
Key takeaways
- Multi-agent architectures fit workflows too complex for a single AI agent, where different steps need different reasoning, tools, or data access.
- We've built these systems for document processing, automated research pipelines, business process automation, and AI data enrichment.
- Most multi-agent projects deliver in 6 to 14 weeks at a fixed cost starting at $20,000.
- We work with OpenAI GPT-4o, Claude, Gemini, Llama, and open-source models, including multi-model combinations.
- Handoff design is where most multi-agent systems fail. We define the output schema and context passing between every agent before writing a line of code.
Trusted by
One agent read every document. It kept getting the details wrong.
A single AI agent took each incoming document, tried to extract the data, judge whether it was valid, and route it onward, all in one pass. On clean inputs it looked fine. On the edge cases it conflated extraction with the decision, lost context across a dozen tool calls, and produced outputs that read as plausible but failed on the details.
Break the work apart and it holds. One agent extracts. A second validates against defined criteria. A third routes. Each agent only has to be good at one thing, so each one is. That orchestrator-worker split now moves 20,000+ transactions a day.
That is not one model with a longer prompt. It is a system of specialised agents, each with a clear role, its own tools, and defined inputs and outputs. The coordination between them is the product.
Multi-agent systems are how production AI handles complexity, and the shift toward them is moving fast: Gartner projects that by 2028 roughly a third of enterprise software applications will include agentic AI, up from less than 1% in 2024. The failure mode of a single AI agent on a complex task is well documented: it conflates research with decision-making, loses context across many tool calls, and produces outputs that look plausible but fail on the details. Assign a specialised agent to each step, each with a clear role, specific tools, its own grounding in your data through a RAG pipeline, and defined inputs and outputs, and the failure modes become easy to isolate and fix.
Bring the agent demo you've already built. We assess what works and keep what's validated, then engineer the orchestration, evals, guardrails, and cost control that a notebook prototype never had. That production discipline is the core of our AI development work. RaftLabs has shipped 100+ products since 2015, rated 4.9/5 on Clutch. The team that scopes your workflow is the team that builds it, no offshore handoff after the contract is signed. Compliance for GDPR, HIPAA, or SOC 2 is scoped in week 1, not retrofitted before launch.
Multi-agent design pays off when the work genuinely needs more than one agent.
Everything on the left should already be true for your workflow. Even one thing on the right, and a single agent or a no-code tool is the smarter first step.
A fit
01
A multi-step workflow where different steps need genuinely different reasoning, tools, or data access.
02
Output quality that needs a validation step: one agent produces, a second checks it against defined criteria before it's used.
03
Enough volume or stakes to justify auditability, with every agent's output logged and inspectable before the next step runs.
Not a fit
- A moderate-complexity task with one consistent type of reasoning throughout, where a single agent handles it.
- A simple Q&A or FAQ responder, where a chatbot is the right tool.
- A workflow that isn't mapped yet, decomposition comes before any agent design.
What we build
What we build
- 01Orchestrator and worker architecturesOrchestrator agents that decompose tasks, delegate to specialist worker agents, and synthesise the results. Orchestrator state persists so long workflows can pause and resume without re-running completed steps, max-iteration guards stop runaway loops and route to a human, and every agent call and handoff is traced so you can see which step went wrong. The stack is LangGraph, AutoGen, or CrewAI, with PostgreSQL and Redis for state and LangSmith for tracing.
- 02Parallel processing agent pipelinesMulti-agent designs that run independent subtasks in parallel rather than sequentially, cutting total processing time. Fan-out patterns dispatch multiple worker agents that scale horizontally as batch sizes grow, and a merge agent synthesises results with explicit conflict resolution because parallel outputs are not always consistent. Fan-out and queueing run on RabbitMQ or AWS SQS/SNS.
- 03Critic and validation agent designsProducer-critic architectures where one agent generates output and a second validates it against a structured checklist before it proceeds, catching errors a single agent would miss. When output keeps failing revision cycles, the system escalates to a human reviewer with the critic's objections attached, the right posture for legal, financial, and medical outputs. Validation results are stored in PostgreSQL and traced through Langfuse.
- 04Tool-using agent networksAgents with specific tool access: a research agent with web search, a data agent with database query, a retrieval agent grounded in your documents, an execution agent with API calls, a document agent with file access. Each agent uses the right tool for its subtask rather than one agent with access to all tools, which makes failure modes easier to isolate, test, and improve.
- 05Human-in-the-loop agent workflowsMulti-agent systems with defined human review checkpoints: the system runs autonomously to a set stage, surfaces the output for review, and continues after approval. Used when partial automation is the right risk posture, agents handle research and drafting while humans review before any external action, with a review UI that gives reviewers full context.
- 06Agent memory and state managementShared memory and context management for multi-agent systems: episodic memory agents can query for prior interactions, vector databases for semantic context retrieval, and structured state that persists across the pipeline. Memory architectures give agents the context they need without overloading every context window with irrelevant history.
How we approach multi-agent development
- 01
Workflow decomposition first
Before designing any agent, we decompose the workflow into its natural subtasks, each step that needs different reasoning, tools, or data access. We identify where handoffs happen, what each step's output schema needs to be, and where human review is needed, because workflow decomposition determines the agent architecture.
- 02
Evaluation framework per agent
Each agent gets its own evaluation framework: test cases for its subtask, metrics for its output type, and a pass threshold before it enters the production pipeline, plus system-level evaluation for end-to-end performance. We don't deploy a multi-agent system without knowing each agent's individual reliability.
- 03
Failure mode and recovery design
Multi-agent systems fail in specific ways: an agent outputs the wrong format, a tool call errors, context gets lost in a handoff. We design recovery into the architecture, retry logic, format validation at handoff boundaries, escalation to human review when the system can't recover, and complete logging for debugging.
- 04
Cost and latency optimisation
Multi-agent systems carry higher inference cost and latency than single agents. We design cost-optimised architectures that use cheaper models for simpler subtasks and frontier models only where reasoning complexity requires them, with parallel processing to cut latency and cost-per-workflow monitoring as usage scales.
Proof it holds up in production
Most multi-agent demos run clean in a notebook and fall over in production. 95% of enterprise GenAI pilots deliver no measurable profit (MIT, The GenAI Divide, 2025). The gap is never the agent prompts, it's the orchestration, evals, guardrails, and cost control around them. That is the part we build.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin, across AI, SaaS, mobile, automation, and enterprise platforms.
Proof
What multi-agent AI looks like in production
- AI products shipped to production
- 20+
- transactions processed in a single day
- 20K+
- client rating on Clutch
- 4.9/5
Complex AI workflows that single agents can't handle reliably
Orchestrator-worker architectures, critic-validation designs, and tool-using agent networks, delivered at a fixed cost.
How it works
From scope to shipped
Every multi-agent project follows four phases. Scope is locked and price is fixed before development starts.
- Week 101
Workflow decomposition and scope
We map the workflow into its natural subtasks, identify which steps need different reasoning or tools, and define the agent architecture. You leave week 1 with a written scope document, agent design, and a fixed-price quote. No development starts without your sign-off.
- Weeks 2-302
Agent design and evaluation framework
We design the prompt, tools, output schema, and test cases for each agent before writing production code. Each agent gets its own evaluation framework, pass threshold, and failure mode coverage. The handoff contracts between agents are locked here.
- Weeks 4-1203
Build, integrate, and QA
Working agent pipeline at a staging URL by the end of sprint one. Each agent is evaluated individually before integration. QA runs in parallel with every sprint, not as a phase at the end. Observability via LangSmith or Langfuse from day one.
- Weeks 12+04
Production launch and post-launch support
Production deployment with monitoring activated on launch day. Cost-per-workflow tracking, failure alerting, and 8 weeks of post-launch support included in every project.
What clients say
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Paula Castro
IrelandReservations Officer, City Break Apartments
Working with RaftLabs has been amazing. The team is super responsive and quick to address our needs. They built a booking platform that's been a game changer for our team and our guests.
01 / 03
Where you land in that range depends on scope, not negotiation:
- Focused build, $15,000-$35,000
- Two to three agents with defined roles, tool integrations, and handoff logic for one specific workflow.
- Complex pipeline, $30,000-$100,000
- Five or more agents, multiple tool integrations, parallel processing, and production monitoring infrastructure.
What it costs
Multi-agent systems, starting at $15,000.
Orchestrator-worker architecture, per-agent evaluation, failure recovery, and cost-per-workflow monitoring, priced as one project.
Starts at $15,000
First phase scoped and priced in about a week. Most clients start here and expand once they've seen it work.
We scope the agent architecture and deliver a go/no-go recommendation before you commit to a full build.
No hourly billing
Once we scope your first phase, that price is locked in writing. No surprise invoices, no change fees you didn't agree to.
Go or no-go first
We scope every project and give you a go/no-go recommendation before you commit to full development, plus 8 weeks of post-launch support in every build.
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Read moreFrequently asked questions
A multi-agent AI system is an architecture where multiple AI agents, each with a specific role, tools, and instructions, work together to complete a complex task. You need one when: (1) A single agent can't reliably complete the full task because it requires different reasoning at different steps, research requires different instructions than decision-making, which requires different instructions than output generation. (2) The task requires parallel processing, multiple agents can work on different parts simultaneously rather than sequentially. (3) Quality requires validation, one agent produces output, a second validates it against defined criteria, a third revises based on the validation. (4) The task requires specialised tools at each step, a research agent uses web search, a data agent queries a database, an execution agent calls APIs. (5) You need auditability, each agent's output is logged and inspectable before the next step proceeds.
An AI agent is a single LLM instance with access to tools that can execute a multi-step task autonomously, it reasons, selects tools, executes tool calls, processes results, and decides next steps in a loop. A multi-agent system coordinates multiple agents, each specialised for a specific sub-task, with defined handoffs between them. A single agent is sufficient for moderate-complexity tasks with a consistent reasoning type throughout. Multi-agent systems are needed when different steps in a task require genuinely different reasoning approaches, when parallelisation matters, or when you need a validation agent to check the primary agent's output before it's used. Most production AI workflows benefit from multi-agent design because it makes failure modes easier to isolate and fix.
Agent handoffs are designed around the information each agent needs to do its job and the format its output needs to take for the next agent to use. We define: the output schema of each agent (structured JSON, prose, a decision signal, a tool call result), the context that gets passed between agents (full history, a summary, specific fields), the error handling when an agent produces an invalid output or fails, and the escalation path when the system can't complete a task autonomously and needs human review. Handoff design is where most multi-agent systems fail, it's not the individual agent prompts that break, it's the assumption about what one agent passes to the next.
A focused multi-agent system, two to three agents with defined roles, tool integrations, and handoff logic for one specific workflow, typically runs $15,000-$35,000. Complex multi-agent pipelines with five or more agents, multiple tool integrations, parallel processing, and production monitoring infrastructure run $30,000-$100,000. Cost depends on workflow complexity, number of agents, tool integrations, and evaluation requirements. We scope every project before pricing it and deliver a go/no-go recommendation before committing to full development.
A focused multi-agent system with two to three agents and one well-defined workflow typically takes 6 to 10 weeks from kick-off to production. More complex pipelines with five or more agents, parallel processing, and production monitoring take 10 to 14 weeks. Timeline depends on workflow complexity, the number of tool integrations required, and how much evaluation data exists for each agent. We deliver a fixed timeline in writing before development starts.
We have built multi-agent systems for healthcare (clinical data processing and patient monitoring), financial services (document extraction and compliance review), logistics (route optimisation and exception handling), and professional services (automated research and report generation). The architecture is workflow-driven, not industry-driven. If your workflow has multiple steps that need different reasoning or different tools at each step, multi-agent design applies. We serve clients in the United States, United Kingdom, Australia, Canada, and Ireland.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Multi-Agent AI Systems in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.