- 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 will evaluate your workflows using MCP-aligned integrations and A2A/ACP communication models to identify the right balance between agent patterns.
- 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.
- Build-vs-Buy 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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ENFORCE DETERMINISTIC EXECUTION
Eliminate ghost loops and unpredictable delegation by forcing every agent through controlled graph paths that guarantee progress under real production load.
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ENFORCE STRICT PERMISSIONS
Run agents inside isolated environments with verified tool access so every decision stays traceable and aligned with corporate governance.
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REDUCE TOKEN COSTS
Reduce LLM costs by routing complex reasoning to heavy models and shifting routine processing to sandboxed Python execution that keeps budgets 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. We build comprehensive evaluation systems and "gold datasets" before writing a single line of production code. For software vendors, this ensures a predictable, bug-free user experience.
Safe Agent Orchestration
Devox Software extends the same governance model to Microsoft-centric enterprises. We build multi-agent orchestration on Microsoft Semantic Kernel, applying the same deterministic execution and audit-logging discipline already proven on Devox's SAP-integrated automation engagement.
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. Based on this structured decomposition, you get 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. Centered on operational stability, you gain workflows that automatically recover from errors, adjust plans safely, and maintain predictable performance under real production load.
- 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. Intended to unify fragmented operations, you receive cohesive workflows that reduce handoffs, eliminate redundant steps, and create measurable efficiency across departmental boundaries.
- Conditional Routing. We will design conditional routing using agent‑to‑agent delegation rules to ensure safe, deterministic hand‑offs between autonomous agents. Made for complex environments, you obtain routing logic that keeps multi-agent coordination predictable at scale.
- 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. Created to maintain governance, you gain controlled intervention points that reduce operational risk, ensure compliance, and keep humans aligned with critical agent actions.
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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. Our team will implement video agents 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. You’ll see integrated outputs that merge modalities cleanly, reduce ambiguity, and support more accurate reasoning than any single-channel agent could achieve alone.
- Use‑case specialization. Our team will tailor multimodal agents to domain‑specific workflows using telemetry patterns, containment‑rate insights, and MCP‑restricted tool sets for safe specialization. Expect purpose‑built flows for general enterprise use cases that deliver measurable efficiency gains without compromising governance or operational stability.
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Autonomous Multi-Agent Operations Framework
- Specialized agent fleets. Our team will design specialized agent fleets using MCP-restricted tool access and A2A/ACP delegation rules to ensure 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 how much independence each process receives. Anchored in governance, you gain adjustable autonomy settings that balance speed with safety and prevent agents from exceeding their authorized operational scope.
- 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 by using 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 the risk of 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. A clearer path to operational reliability: you gain automated inspection cycles, early anomaly detection, and reduced downtime without compromising governance or safety boundaries.
- Revenue Cycle. Our team will implement revenue‑cycle agents using VPC‑isolated execution, MCP‑scoped permissions, and controlled multi‑step reasoning to safely handle essential tasks in the revenue cycle.
- 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. You’ll see faster triage, cleaner evidence organization, and more stable assessment outcomes driven by multi-agent reasoning that avoids hallucination and circular delegation.
- Retail Demand Planning. Our team will create retail‑planning agents that use multi‑source retrieval, structured forecasting logic, and Code Mode pipelines to support inventory and demand. Expect more accurate forecasts, less stock volatility, and clearer pricing signals from agents that safely coordinate across large, fast-changing retail datasets.
WORKFLOW MANAGEMENT
Green Space Pro: Franchise Management Platform for a Highly-Regulated Industry
A centralized digital workspace for cannabis franchise vendors and regulators to manage operations, ensure compliance, and streamline regulatory communication in a highly regulated industry.
Additional Info
Core Tech:
- Svelte.js
- Node.js
- REST API
- CI/CD
- Progressive Web App (PWA)
- manual and automated QA
Country:
USA
AI
AI Content Platform That Researches Before It Writes
A research and content platform built for a US mid-market B2B software company to help the Head of Content turn complex technical briefs into source-grounded, publish-ready content.
Additional Info
Core Tech:
- LLMs
- RAG
- Web Search
- Query Decomposition
- Retrieval Pipelines
- Prompt Orchestration
- Source Attribution
Country:
United States
Modular LMS for Multi-Domain Learning: SwissMentor’s Enterprise-Grade Online Platform
A full-featured learning management system built to digitize education workflows, manage courses, and support online learning at scale.
Additional Info
Core Tech:
- .NET Core
- PostgreSQL
- Angular
- Docker
- Kubernetes
- Azure
- SCORM
Country:
Switzerland
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
Industry Trends
Manufacturing
Transforming the Oil and Gas Industry with AI Magic
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Industry Trends
Software Development
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AI Solutions
Legacy Modernization
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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 such as the Model Context Protocol (MCP) to connect modern AI reasoning engines with 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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How do you prevent agent loops?
Unstructured agents can enter execution loops, passing tasks back and forth without making progress. We prevent this by using deterministic graph frameworks such as LangGraph. The system must either resolve the task within a defined step limit or escalate it to a human reviewer.
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Will we be locked into one AI provider?
No. We design model-agnostic architectures. Frameworks such as LangGraph and AutoGen allow us to change 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 data and behavior changes. If an agent’s accuracy declines because of shifting inputs or user behavior, the system detects the issue and initiates 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 -
LangGraph vs CrewAI vs AutoGen vs Semantic Kernel – which orchestration framework fits our stack?
It depends on your existing stack and workflow shape. LangGraph is the default for deterministic, loop-safe execution graphs. CrewAI fits role-based agent teams. AutoGen suits conversational, multi-perspective reasoning tasks. Semantic Kernel is the right fit for .NET/Azure-standardized enterprises, especially alongside SAP. Devox Software’s framework-fit assessment compares all four against your infrastructure before committing to a build.
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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 27001:2013 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. This modular AI services stack approach means adding an agent, swapping a framework, or upgrading a model doesn’t require touching the rest of your enterprise IT- the same pattern used on Devox Software’s SAP-integrated automation engagement.
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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 on production systems. 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, cutting token costs by up to 90% in production.
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How much does a multi-agent development engagement typically cost, and how long does it take?
Scope, framework choice, and compliance requirements (VPC/on-prem vs. cloud) are the main cost drivers. A multi-agent readiness assessment typically runs first to size the build and de-risk the framework decision before committing to a full engagement.
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