• 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?

Multi-Agent AI Systems

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

RaftLabs builds multi-agent AI systems for complex workflows that require specialised AI agents working in coordination -- research agents, decision agents, execution agents, and validation agents with defined handoffs and shared context. Multi-agent architectures are used when a single AI agent can't reliably complete a task because it requires different reasoning capabilities, tools, or data access at each step. We've built multi-agent systems for document processing, automated research pipelines, business process automation, and AI-powered data enrichment. Most multi-agent projects deliver in 6--14 weeks at a fixed cost.

Vodafone
Aldi
Nike
Microsoft
Heineken
Cisco
Calorgas
Energia Rewards
GE
Bank of America
T-Mobile
Valero
Techstars
East Ventures

One agent can reason. Multiple agents can collaborate.

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. Breaking the task into specialised agents -- each with a clear role, specific tools, and defined inputs and outputs -- produces more reliable results because each agent only has to be good at one thing.

Multi-agent systems are how production AI handles complexity.

What we build

Orchestrator and worker architectures

Orchestrator agents that decompose tasks, delegate to specialist worker agents, and synthesise the results. Worker agents specialised for specific subtasks -- web research, database queries, document analysis, data transformation, API calls, and output generation. The orchestration layer that coordinates agent work and handles the handoffs that make the system reliable.

Parallel processing agent pipelines

Multi-agent designs that run subtasks in parallel rather than sequentially -- reducing total processing time for workflows where independent subtasks can proceed simultaneously. Fan-out patterns that dispatch multiple worker agents and a merge agent that synthesises results when all workers complete. Used for document analysis across large document sets, multi-source research, and batch processing pipelines.

Critic and validation agent designs

Producer-critic architectures where one agent generates output and a second agent validates it against defined criteria before it proceeds -- catching errors that a single agent making the judgment itself would miss. Used for document extraction (extraction agent plus accuracy validator), content generation (writer plus editor), and decision workflows (decision agent plus compliance validator).

Tool-using agent networks

Agents with specific tool access: a research agent with web search, a data agent with database query, an execution agent with API call capabilities, a document agent with file system access. Agents that use the right tool for their specific subtask rather than one agent with access to all tools. Tool-specific agents are easier to evaluate, test, and improve because failure modes are isolated.

Human-in-the-loop agent workflows

Multi-agent systems with defined human review checkpoints -- the system runs autonomously to a defined stage, surfaces the output to a human for review, and continues after approval. Used when partial automation is the right risk posture: agents handle the research and drafting, humans review before any external action. Human review UI integrated with the agent pipeline so reviewers get the full context.

Agent memory and state management

Shared memory and context management for multi-agent systems -- episodic memory that agents can query for prior interactions, vector databases for semantic context retrieval, and structured state that persists across the agent pipeline. Memory architectures that give agents access to the context they need without overloading every agent's context window with irrelevant history.

Multi-agent AI systems built for production workflows, not demos

Orchestrator-worker architectures, critic-validation designs, and tool-using agent networks. Fixed cost delivery.

How we approach multi-agent development

Workflow decomposition first

Before designing any agent, we decompose the workflow into its natural subtasks -- each step that requires different reasoning, different tools, or different data access. We identify where handoffs happen, what the output schema of each step needs to be, and where human review is needed. Workflow decomposition determines the agent architecture.

Evaluation framework per agent

Each agent in the system gets its own evaluation framework -- test cases for its specific subtask, metrics for its specific output type, and a pass threshold before it's included in the production pipeline. System-level evaluation for end-to-end performance. We don't deploy a multi-agent system without knowing each agent's individual reliability.

Failure mode and recovery design

Multi-agent systems fail in specific ways: an agent produces an output in the wrong format, a tool call returns an error, context gets lost in a handoff. We design failure 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. Production multi-agent systems need to handle failure gracefully.

Cost and latency optimisation

Multi-agent systems have higher inference costs and latency than single agents. We design cost-optimised architectures: using cheaper models for simpler subtasks and frontier models only where reasoning complexity requires them. Parallel processing where possible to reduce end-to-end latency. Cost-per-workflow monitoring so you can track inference costs as usage scales.

Complex AI workflows that single agents can't handle reliably

Multi-agent architectures for research, decision-making, validation, and execution workflows. Fixed cost.

Let's talk about your project

Tell us the workflow you're trying to automate and where a single agent has failed. We'll design the right agent architecture and give you a fixed cost.

Frequently 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 $20,000--$60,000. Complex multi-agent pipelines with five or more agents, multiple tool integrations, parallel processing, and production monitoring infrastructure run higher. 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.