- Use-case discovery. Our team will analyze operational data to identify where autonomous agents can reliably deliver measurable operational value. You receive a suitability matrix that ranks agentic automation opportunities by key criteria.
- Architecture design. We decide how many agents the workflow needs, what each one does, and where one agent hands work to the next. Often the answer is fewer agents than expected.
- Framework selection. Our team will compare LangGraph, CrewAI, and AutoGen through key architectural criteria. You avoid misalignment by receiving a framework‑fit assessment that highlights trade‑offs, infrastructure expectations, and long‑term maintenance implications.
- Cost Modeling. We will model cost envelopes using Code Mode token‑reduction benchmarks, infrastructure requirements, and scaling scenarios, including VPC isolation for sensitive workloads. To support informed decisions, you receive a cost-range model with financial and operational forecasts tied to your environment.
- Phased build roadmap. Our team will design a staged rollout plan using MCP governance patterns to guide the transition from PoC to production. The roadmap gives you a controlled rollout plan with milestones, risk gates, and expected impact by phase.
Multi-Agent System Development
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DIVIDE WORK BETWEEN AGENTS
Give each agent one role, its own tools, and a clear point where it hands work to the next. Send any task that stalls to a person instead of letting agents pass it back and forth. -
GOVERN EVERY HANDOFF
Connect agents to your systems through MCP gateways and let them exchange tasks over A2A. Scope each permission to your existing roles and log every handoff for audit. -
MATCH EACH AGENT TO ITS MODEL
Combine agents built on LangGraph, CrewAI, or Semantic Kernel in one system. Route reasoning to frontier models and run routine steps as sandboxed scripts to keep cost per task predictable.
Why choose Devox Software?
What We Offer
Loop-Safe Execution
Unstructured agents can get stuck in ghost loops, delegating tasks back and forth while consuming unnecessary compute. We utilize deterministic graph frameworks (like LangGraph) to force agents down predictable paths.
Production Evals
Deploying AI without a strong evaluation framework increases the risk of production hallucinations. Before development starts, we turn your past, correctly handled cases into a gold dataset. The system has to pass it before release, and again after every change to an agent or a model.
Built for Microsoft Stacks Too
For companies on .NET, Azure, and SAP, we build multi-agent orchestration on Microsoft Semantic Kernel, with the same step limits and audit logging we used on an SAP-integrated automation project.
Token Cost Control
Running frontier LLMs for routine data sorting can quickly inflate cloud costs. We implement a hybrid "Code Mode" routing architecture. Complex reasoning goes to heavy models, while routine formatting is handled by lightweight, auto-generated Python scripts inside a secure sandbox.
What We Deliver
Services We Provide
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Multi-Agent Readiness Assessment
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Agentic Workflow Orchestration System
- Autonomous Task Planning. Our team will build autonomous planners that use MCP‑aligned tool discovery and multi‑agent reasoning to break complex goals into executable steps. This structured decomposition gives you a transparent task graph that clarifies dependencies, reduces ambiguity, and improves reliability across long‑running workflows.
- Multi‑step execution with self‑correction. We will implement multi‑step execution loops using failure‑aware replanning to prevent complex failure modes.
- Cross‑functional workflows. Our team will orchestrate cross‑functional flows using A2A/ACP communication patterns and MCP‑controlled tool access to coordinate core business processes end‑to‑end.
- Conditional Routing. We will design conditional routing with agent‑to‑agent delegation rules to ensure safe, deterministic hand‑offs between autonomous agents.
- Human Review Gates. Our team will embed approval gates and exception-handling paths using strict MCP permissioning, loop limits, and sandboxed execution for sensitive or high-risk decision points.
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Multi-Modal Multi-Agent Platform
- Language Agents. We will develop text agents using multi-step reasoning, controlled context windows, and MCP-aligned extraction tools to deliver stable NLP, summarization, and structured outputs. You gain dependable language automation with high-quality extraction, concise summaries, and lower hallucination risk across enterprise text.
- Stream Processing Agents. We build agents that analyze video and other live streams that use asynchronous multi-agent coordination and controlled tool calls to support real-time stream analysis. You will achieve reliable event detection, structured annotations, and stable performance under changing video workloads.
- Cross-modal fusion. We will design cross-modal fusion layers that combine multiple data modalities using A2A/ACP coordination.
- Use‑case specialization. We tune agents to your domain: your document types, your terminology, and the tools each step needs. Each agent gets only the tools for its own task.
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Autonomous Multi-Agent Operations Framework
- Specialized agent fleets. Our team will design specialized agent fleets with MCP-restricted tool access and A2A/ACP delegation rules, ensuring each unit operates within tightly defined boundaries. You will receive focused autonomous units that execute domain-specific tasks predictably and stay stable under enterprise load.
- Controlled autonomy levels. We will implement controlled autonomy levels using permission tiers, loop‑limit enforcement, and sandboxed execution to tune each process’s independence.
- Human Oversight. Our team will embed failover and escalation mechanisms with strict MCP permissioning, approval gates, and structured exception routing to keep humans in meaningful control.
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Governed Agent Deployment
- Agent Access Controls. Our team will implement least-privilege access with strict permission boundaries to prevent unauthorized agent actions. You’ll gain visibility into a governed permission model that limits blast radius, enforces clear access tiers, and keeps every agent operating within tightly defined corporate policies.
- Compliance enablement. We will align your multi-agent environment with SOC 2, HIPAA, and GDPR expectations by using controlled data flows, VPC-isolated execution, and immutable decision-logging patterns. You’ll cut compliance overhead with structured controls that maintain verifiable adherence across sensitive workflows.
- Decision Traceability. Our team will generate immutable audit trails using MCP‑aware logging and structured state capture to record every agent decision and tool invocation. You’ll avoid opaque behavior by receiving full‑fidelity decision logs that support investigations, compliance reviews, and long‑term accountability across autonomous operations.
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Industry-Specific Multi-Agent Solutions
- Supply Chain. Our team will build supply-chain agents that use MCP-restricted tool access, A2A coordination, and Code Mode orchestration to manage core logistics tasks with predictable autonomy. You will receive coordinated logistics flows that reduce manual routing, improve responsiveness, and stay stable during peak demand.
- Manufacturing. We will design manufacturing agents that use structured inspection logic, sandboxed execution, and telemetry-driven reasoning to support quality checks and predictive maintenance across production lines.
- Revenue Cycle. We build agents for healthcare revenue cycle work, such as claim preparation, denial review, and payment posting, running in an isolated VPC. Your billing team handles exceptions instead of every claim.
- Insurance Claims. We will build insurance-claims agents that use structured retrieval, relevance filtering, and guarded decision flows to help with assessment and fraud detection.
- Retail Demand Planning. Our team will build retail‑planning agents that use multi‑source retrieval, structured forecasting logic, and Code Mode pipelines to support inventory and demand planning.
Quality Engineering / Software Testing
AI Platform That Generates QA Documentation From an Existing Test Suite
A QA lead had three weeks to prove what her test suite covered. Rebuilding it by hand exposed the bigger problem. That led to a platform that reconstructs QA documentation from the test suite, its linked tickets, and run history.
Additional Info
Core Tech:
- LLMs (local and hosted)
- RAG
- Test Suite Parsing
- AST Analysis
- Traceability Graph
- Jira & Test Management Integrations
- CI/CD Hooks
- Structured Outputs
- Vector Search
- Log Ingestion
Joynd: Unified Integration Platform for HR Software Providers
A robust B2B platform that connects companies and HR software providers through federated identity, intelligent workflows, and secure data integrations.
Additional Info
Core Tech:
- Angular
- NgRx
- RxJS
- Tailwind CSS
- .NET Core
- PostgreSQL
- AWS
- Docker
Country:
USA
Testimonials
Testimonials
Sweden
The solutions they’re providing is helping our business run more smoothly. We’ve been able to make quick developments with them, meeting our product vision within the timeline we set up. Listen to them because they can give strong advice about how to build good products.
Carl-Fredrik Linné
Tech Lead at
CURE Media
United States
We are a software startup and using Devox allowed us to get an MVP to market faster and less cost than trying to build and fund an R&D team initially. Communication was excellent with Devox. This is a top notch firm.
Darrin Lipscomb
CEO, Founder at
Ferretly
Australia
Their level of understanding, detail, and work ethic was great. We had 2 designers, 2 developers, PM and QA specialist. I am extremely satisfied with the end deliverables. Devox Software was always on time during the process.
Daniel Bertuccio
Marketing Manager at
Eurolinx
Australia
We get great satisfaction working with them. They help us produce a product we’re happy with as co-founders. The feedback we got from customers was really great, too. Customers get what we do and we feel like we’re really reaching our target market.
Trent Allan
CTO, Co-founder at
Active Place
United Kingdom
I’m blown up with the level of professionalism that’s been shown, as well as the welcoming nature and the social aspects. Devox Software is really on the ball technically.
Andy Morrey
Managing Director at Magma Trading
Switzerland
Great job! We met the deadlines and brought happiness to our customers. Communication was perfect. Quick response. No problems with anything during the project. Their experienced team and perfect communication offer the best mix of quality and rates.
Vadim Ivanenko
COO at
Optherium
United States
The project continues to be a success. As an early-stage company, we're continuously iterating to find product success. Devox has been quick and effective at iterating alongside us. I'm happy with the team, their responsiveness, and their output.
Sweden
We hired the Devox team for a complicated (unusual interaction) UX/UI assignment. The team managed the project well both for initial time estimates and also weekly follow-ups throughout delivery. Overall, efficient work with a nice professional team.
John Boman
Product Manager at
Lexplore
Canada
Their intuition about the product and their willingness to try new approaches and show them to our team as alternatives to our set course were impressive. The Devox team makes it incredibly easy to work with, and their ability to manage our team and set expectations was outstanding.
Tamas Pataky
Head of Product at
Stromcore
Estonia
Devox is a team of exepctional talent and responsible executives. All of the talent we outstaffed from the company were experts in their fields and delivered quality work. They also take full ownership to what they deliver to you. If you work with Devox you will get actual results and you can rest assured that the result will procude value.
Stan Sadokov
Product Lead at
Multilogin
United Kingdom
The work that the team has done on our project has been nothing short of incredible – it has surpassed all expectations I had and really is something I could only have dreamt of finding. Team is hard working, dedicated, personable and passionate. I have worked with people literally all over the world both in business and as freelancer, and people from Devox Software are 1 in a million.
Mark Lamb
Technical Director at M3 Network Limited
Insights
Our Experts' Insights
AI & ML
AI Solutions
Legacy Modernization
Intelligent Document Processing vs Traditional OCR: What Enterprises Need in 2026
AI & ML
AI Solutions
Industry Trends
Startups
Revolutionizing Healthcare with AI Startups You Need to Follow
FAQ
Frequently Asked Questions
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Can agents work with legacy systems?
Yes. You do not need to modernize your entire backend to deploy AI. We use open standards like the Model Context Protocol (MCP) to connect modern AI reasoning engines to legacy on-premises systems. This allows agents to read from and write to older databases securely within defined boundaries, reducing integration effort and risk.
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Will we be locked into one AI provider?
No. We design model-agnostic architectures. Frameworks such as LangGraph and AutoGen let us switch foundation models based on the task. For example, we may route complex reasoning to Anthropic Claude and routine extraction to a more cost-efficient open-source model. This reduces vendor dependence and gives you more control over operating costs.
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How do you manage model drift?
AI performance can decline over time if it is not monitored. We implement observability and drift-detection pipelines to track changes in data and behavior. If an agent’s accuracy declines due to shifting inputs or user behavior, the system detects the issue and triggers retraining or prompt optimization. This helps maintain stable operations without requiring a large internal MLOps function.
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What are your orchestration workflows?
Framework Best for How Devox applies it Governance model LangGraph Deterministic, graph-based execution where loop prevention and predictable state transitions matter most Default choice for loop-safe execution Step-limit enforcement; forced human escalation on failure CrewAI Role-based agent teams modeled on a human org chart (researcher, writer, reviewer, etc.) Used when a workflow maps naturally to defined roles and sequential hand-offs Role-scoped permissions per agent AutoGen Conversational, debate-style multi-agent reasoning for exploratory or research-heavy tasks Applied where a decision benefits from multiple agent perspectives before finalizing Conversation-turn limits with a human review checkpoint Microsoft Semantic Kernel Enterprises standardized on .NET/C# and Azure, especially alongside SAP or legacy .NET systems Used on Microsoft-stack engagements, including Devox’s SAP-integrated automation build Native Azure AD/RBAC integration; plugin-scoped permissions -
What do MCP vs. A2A protocols actually govern?
Model Context Protocol (MCP) Agent2Agent Protocol (A2A) What it standardizes How a single agent discovers, authenticates to, and calls tools or data sources How independent agents exchange tasks and results Problem it solves Tool sprawl and inconsistent context-passing inside one agent Vendor lock-in and framework fragmentation across a multi-agent system Governance unit Per-tool permission scope Per-agent communication contract You need it when Any agent touching external systems, databases, or portals A system has more than one agent, especially across frameworks or vendors You need both when Enterprise multi-agent systems that integrate legacy databases/portals and coordinate multiple specialized agents -
What is the difference between MCP and A2A, and do we need both?
The Model Context Protocol (MCP) standardizes how one agent accesses tools and data sources. The Agent2Agent Protocol (A2A) governs communication between separate agents, including those built on different frameworks or by different vendors. Most enterprise multi-agent systems need both: MCP for safe tool and database access, and A2A for cross-agent coordination without vendor lock-in.
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Can a multi-agent system integrate with government or legacy databases and portals?
Yes. Devox Software connects multi-agent systems to existing databases and portals through MCP-governed, permission-scoped gateways rather than direct access. Legacy schemas stay untouched, agent read/write rights map to your existing RBAC, and every interaction is logged for audit, consistent with ISO/IEC 27001:2022 controls.
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How do you deploy a modular AI services stack inside existing enterprise IT without disrupting current systems?
Each agent, tool connector, and evaluation layer is built as an independently replaceable component behind MCP-governed interfaces. You can add an agent, swap a framework, or upgrade a model without touching the rest of your systems. We used the same approach on an SAP-integrated automation project.
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What does human-in-the-loop approval look like in production?
New agents run in shadow mode first, processing live data without write access. Hard-coded approval gates require human sign-off before any agent can act in production. If confidence drops, data quality degrades, or a schema mismatch appears, the agent pauses and escalates automatically instead of guessing.
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How do you prevent infinite agent loops and runaway token costs?
LangGraph-based deterministic graphs force every agent through a bounded number of steps; the task either resolves within that limit or escalates to a human. On the cost side, Devox Software’s Code Mode routing sends routine formatting and data-sorting work to sandboxed Python scripts instead of frontier-model calls.
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