Complex enterprise workflows don't fit a single agent. Perimattic designs and builds multi-agent systems where specialist agents collaborate, delegate tasks to each other, and complete workflows that span every department and system in your business.
Orchestration Frameworks and Foundation Models We Build Multi-Agent Systems With
A multi-agent AI system is an architecture where a lead orchestrator agent decomposes a complex goal into sub-tasks and delegates each to a specialist agent — each with its own tools, instructions, and context. The specialist agents execute their sub-tasks, communicate results to the orchestrator and to each other through shared state, and the orchestrator synthesises the final output. The result is a system that handles workflows too broad, too complex, or too multi-domain for any single agent to manage reliably.
The distinction matters in practice. A single agent handling a complex procurement workflow must simultaneously maintain context about supplier policies, approval thresholds, contract terms, and purchasing history — all within one context window, using one tool set. A multi-agent system assigns each domain to a specialist: a research agent gathers supplier data, an analysis agent checks contract terms, a compliance agent validates approval thresholds, and an execution agent triggers the purchase order. Each agent works with focused context, the right tools, and clear scope.
Perimattic designs and builds both single-agent and multi-agent systems, and is framework-agnostic across LangGraph, CrewAI, AutoGen, Agno, and Haystack. We start every engagement with the architecture that fits the workflow — not the one we know best.
Task complexity
Suited to focused, bounded tasks with one tool set
Task complexity
Handles complex, multi-domain workflows spanning multiple systems
Parallel execution
Sequential steps only — one action at a time
Parallel execution
Independent sub-tasks run concurrently across specialist agents
Specialisation
One generalised agent for all steps and domains
Specialisation
Dedicated specialist agents per domain with focused context
Context scope
Limited to a single context window — complex tasks exceed it
Context scope
Distributed context across multiple agents, each with focused scope
Failure isolation
One failure stops the entire workflow
Failure isolation
Agent failures isolated, orchestrator retries or reroutes gracefully
The choice between a single agent and a multi-agent system is an architectural decision, not a preference. We make that determination based on your workflow's complexity, the number of independent sub-tasks, and the number of systems involved — before a single line of code is written.
Seven specialist service lines covering every layer of a production multi-agent system.
We design the complete multi-agent architecture for your workflow: orchestrator logic, specialist agent roles, tool registries, shared memory strategy, communication protocols, and failure handling at every layer. The architecture is designed for production from day one, not retrofitted from a demo.
We build the orchestrator agent that manages the entire workflow: task decomposition, agent selection, sub-task delegation, progress tracking, conflict resolution, and output synthesis. Every orchestrator includes fallback logic, retry strategies, and human-in-the-loop escalation points for high-risk decisions.
We build the specialist agents that execute specific sub-tasks within your workflow. Each specialist has its own tool set, system prompt, context window strategy, and evaluation criteria. Common specialists include research agents, analysis agents, execution agents, validation agents, and summarisation agents.
We design and implement the communication layer between agents: shared state graphs in LangGraph, structured message queues in AutoGen, or custom handoff protocols for hybrid architectures. We manage context windows so agents receive only the information they need, minimising token costs and reducing hallucination risk.
We automate complete enterprise workflows using multi-agent networks. Finance reconciliation, supply chain co-ordination, clinical documentation, compliance audit, and sales intelligence workflows are all candidates where a single agent's scope or context window would be insufficient.
We connect your multi-agent system to the enterprise tools it needs to act on: ERPNext, Salesforce, HubSpot, SAP, SharePoint, SQL databases, vector stores, and custom REST APIs. Every integration includes authentication, rate limiting, data validation, and audit logging.
We instrument every agent in the system with tracing, latency monitoring, accuracy evaluation, and cost tracking. We build dashboards showing what every agent decided, why, and what the result was — so you can diagnose failures, measure improvement, and demonstrate ROI.
A structured six-stage process from free scoping session to live deployment and ongoing optimisation.
We map the workflow you want to automate, identify how many specialist agents it requires, and design the high-level multi-agent architecture. We assess integration complexity and identify data quality risks. This session is free and carries no obligation.
We produce the detailed architecture: orchestrator design, specialist agent roles, tool registries, shared state model, communication protocol, and failure handling strategy. We select the orchestration framework based on your workflow's structure.
We build working prototypes of the orchestrator and each specialist agent, then test the handoff logic between them against real data. This stage surfaces co-ordination failures, tool errors, and context management issues before the production build begins.
We build the full production multi-agent system and connect it to your enterprise systems. Authentication, rate limiting, guardrails, human-in-the-loop escalation points, and comprehensive logging are built in from the start, not added after.
We test the system against real production scenarios including adversarial inputs, partial agent failures, and edge cases. We establish evaluation baselines for correctness, completeness, efficiency, and reliability before go-live.
We deploy the system to your infrastructure with full observability tooling. We monitor agent decisions, latency, cost, and accuracy in the first weeks and help you plan capacity optimisation, new specialist agents, and workflow extensions.
We map the workflow you want to automate, identify how many specialist agents it requires, and design the high-level multi-agent architecture. We assess integration complexity and identify data quality risks. This session is free and carries no obligation.
We produce the detailed architecture: orchestrator design, specialist agent roles, tool registries, shared state model, communication protocol, and failure handling strategy. We select the orchestration framework based on your workflow's structure.
We build working prototypes of the orchestrator and each specialist agent, then test the handoff logic between them against real data. This stage surfaces co-ordination failures, tool errors, and context management issues before the production build begins.
We build the full production multi-agent system and connect it to your enterprise systems. Authentication, rate limiting, guardrails, human-in-the-loop escalation points, and comprehensive logging are built in from the start, not added after.
We test the system against real production scenarios including adversarial inputs, partial agent failures, and edge cases. We establish evaluation baselines for correctness, completeness, efficiency, and reliability before go-live.
We deploy the system to your infrastructure with full observability tooling. We monitor agent decisions, latency, cost, and accuracy in the first weeks and help you plan capacity optimisation, new specialist agents, and workflow extensions.
Select a domain to see how orchestrator-specialist architectures handle workflows that exceed what any single agent can manage.
Multi-agent research systems assign parallel retrieval, analysis, and synthesis tasks to specialist agents — delivering structured intelligence reports from dozens of sources in minutes rather than the hours a human research team would need. The orchestrator manages source selection, deduplication, and conflict resolution before synthesising the final output.
Multi-agent research systems assign parallel retrieval, analysis, and synthesis tasks to specialist agents — delivering structured intelligence reports from dozens of sources in minutes rather than the hours a human research team would need. The orchestrator manages source selection, deduplication, and conflict resolution before synthesising the final output.
Complex enterprise workflows that cross ERP, CRM, HRMS, and communication systems are exactly where single agents fail and multi-agent networks succeed. Each specialist agent owns one system and one set of responsibilities — the orchestrator co-ordinates the handoffs, resolves conflicts, and drives the workflow to completion without human co-ordination at each step.
Compliance reviews that require expertise across legal, financial, technical, and regulatory domains are a natural fit for multi-agent architectures. Specialist agents work each domain simultaneously, the orchestrator tracks findings across all agents, and a validation agent cross-checks conclusions before the final compliance report is produced.
Sales intelligence workflows that require prospect research, personalised outreach, competitive positioning, and proposal drafting are too broad for a single agent. Multi-agent systems assign each task to a specialist, run independent workstreams in parallel, and deliver a complete, personalised sales package faster than any human preparation process.
Engineering teams use multi-agent pipelines to automate code review, test generation, documentation, and deployment preparation — tasks that consume developer time without requiring the creative judgement that humans should be spending on architecture and high-value design work.
Clinical workflows that involve patient data, clinical guidelines, insurance requirements, and care team co-ordination are too sensitive and multi-domain for a single agent. Multi-agent systems handle each domain with a specialist, enforce strict access controls, and log every decision for audit — reducing administrative burden while maintaining clinical governance.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade multi-agent engineering from demo-quality builds.
We do not design demo architectures and rebuild them for production. Every multi-agent system we design includes state management, failure handling, retry logic, and observability from the first design session. The architecture your team sees in the scoping call is the one that goes to production.
We select the orchestration framework that fits your workflow's structure — LangGraph for stateful graph-structured workflows, CrewAI for role-based collaborative systems, AutoGen for code-heavy tasks, Agno for lightweight high-performance pipelines. The right framework for the right architecture, every time.
Multi-agent systems are only valuable when connected to the systems your business runs on. We have deep experience wiring agent networks into ERPNext, Salesforce, HubSpot, SharePoint, SAP, custom REST APIs, SQL databases, and vector stores. Integration is part of the architecture from day one.
The team that designs your multi-agent architecture also builds, tests, and deploys it. No hand-off from strategy to delivery. No context lost between design and engineering. The people who understand why the architecture was designed that way are the ones who implement and ship it.
“Unlike boutique dev shops that build to spec without strategic context, or large consultancies that advise without shipping, Perimattic brings both capabilities to every engagement.”
A multi-agent AI system is an architecture where two or more AI agents work collaboratively to complete tasks. A lead orchestrator agent receives the overall goal, breaks it into sub-tasks, and delegates each to a specialist agent. Each specialist has its own tools, context window, and instructions optimised for its domain. The orchestrator collects results, resolves conflicts, and synthesises a final output. This pattern handles workflows that exceed the capacity, tool set, or reasoning scope of any single agent.
A single agent works well for focused, bounded tasks: answering questions from a knowledge base, extracting data from a document, or executing a specific workflow with clear inputs and outputs. A multi-agent system is the right choice when tasks are too broad for one context window, when parallel execution of independent sub-tasks would reduce total time, when different sub-tasks require different specialist tools or LLMs, or when you need independent validation of outputs before they reach the final user.
An orchestrator agent is the supervisor in a multi-agent system. It receives the top-level goal, creates a plan by decomposing the goal into sub-tasks, assigns each to the appropriate specialist agent, monitors progress, handles failures and retries, and synthesises the final output from all specialist results. The orchestrator does not execute sub-tasks itself — it manages the workflow and makes routing decisions based on the current state of the overall task.
Our primary choice for stateful multi-agent systems is LangGraph, which provides explicit graph-based control flow that makes agent routing and state management predictable in production. For role-based collaborative systems we use CrewAI. For code-heavy multi-agent tasks we use AutoGen and Agno. We are framework-agnostic and select based on your workflow's structure, not our defaults. We have production experience with all major multi-agent frameworks including OpenAI Swarm and Haystack.
Agents communicate through shared state, structured message passing, or both. In LangGraph systems, agents read from and write to a shared state graph that the orchestrator maintains. In message-passing architectures like AutoGen, agents send structured messages to each other and the orchestrator routes them. We design the communication protocol for each system based on the workflow's requirements: synchronous handoffs for sequential processes, asynchronous queues for parallel workloads, and shared memory for context that multiple agents need simultaneously.
A proof-of-concept multi-agent system with two to three specialist agents typically takes three to five weeks. A production system with full integration, error handling, observability, and four or more specialist agents typically takes ten to twenty weeks depending on the number of integrations and workflow complexity. We provide a detailed estimate after the free scoping call, once we understand the scope of your workflow and the systems the agents need to connect to.
A focused two-agent system for a specific workflow starts from around USD 25,000. A full multi-agent system with four or more specialist agents, enterprise integrations, and production-grade observability is typically USD 50,000 to USD 150,000. Costs depend on the number of agents, the complexity of inter-agent communication, the number of external integrations, and the observability and evaluation infrastructure required. We scope every engagement before quoting so there are no surprises.
We build failure handling into every layer of the multi-agent architecture. Orchestrators detect failed specialist agents, retry with modified instructions, route to a fallback agent, or escalate to a human-in-the-loop checkpoint. Every agent has maximum step limits, input and output validation, and circuit breakers that prevent runaway execution. We include comprehensive logging of every agent decision so that failures can be diagnosed and reproduced in development rather than discovered in production.
Yes, and parallel execution is one of the primary performance benefits of multi-agent architecture. When a workflow has independent sub-tasks — for example, a research agent gathering market data at the same time a financial agent runs historical analysis — both can execute concurrently. The orchestrator waits for all parallel branches to complete before synthesising the final output. LangGraph and AutoGen both support parallel agent execution natively, and we design workflows to maximise parallelism wherever tasks are genuinely independent.
Industries with complex, multi-domain workflows that span multiple systems and data sources see the greatest benefit. Financial services use multi-agent systems for automated due diligence, credit underwriting, and compliance review. Healthcare organisations use them for prior authorisation, clinical documentation, and care co-ordination. Professional services firms use them for research synthesis, contract analysis, and proposal generation. Any industry where knowledge workers spend significant time co-ordinating information across multiple systems is a strong candidate for multi-agent automation.
We evaluate multi-agent systems across four dimensions: correctness (does the system produce the right output for a given input, measured against a labelled test set), completeness (does the orchestrator successfully decompose and complete the task without dropping sub-tasks), efficiency (is the system using the minimum number of LLM calls and agent steps), and reliability (does the system handle adversarial inputs, partial failures, and edge cases without crashing). We establish baselines before go-live and instrument every agent with LangSmith or LangFuse tracing for continuous production evaluation.
Tell us about the workflow you want to automate and we will design the multi-agent architecture that handles it — from orchestrator logic and specialist agent roles through to integration, observability, and go-live.