- 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, A2A/ACP communication models, and Code Mode orchestration to determine the right balance between agent patterns. You gain a precise architecture blueprint with boundaries, delegation flows, and safeguards that prevent ghost loops and hidden failure chains.
- 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 readiness signals, MCP governance patterns, and Code Mode orchestration 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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GUARANTEE SAFE ACTIONS UNDER 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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CUT TOKEN SPEND THROUGH CODE-MODE ROUTING
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. This guarantees the reliability required for high-SLA software and critical supply chains.
Safe Agent Orchestration
Multi-agent systems can fail when agents share state incorrectly or work toward conflicting goals. We build isolated execution environments and structured inter-agent communication protocols. When one agent hands work to another, the exchange stays isolated, traceable, and aligned with the right context.
Production Evals
Deploying AI without a strong evaluation framework increases the risk of production hallucinations. We build comprehensive evaluation systems (evals) and “gold datasets” before writing a single line of production code. For software vendors, this ensures a predictable, bug-free user experience. For manufacturers, it guarantees that supply chain decisions made by the AI are mathematically verified before execution.
Data Resilience
Enterprise data is never perfect. When a basic bot encounters a broken API or missing inventory record, it may return unreliable output. We engineer agents with robust exception handling. If data schemas don’t match or confidence drops, the agent automatically pauses and escalates to a human manager, preventing catastrophic business errors.
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, cutting token costs by up to 92.8%.
MCP Integration
Connecting AI shouldn’t require a massive backend overhaul. Using the Model Context Protocol (MCP), we create secure, permission-aware connections. Whether you run a modern SaaS platform or a 20-year-old on-premise ERP, we integrate the AI layer without breaking your existing tech stack.
Agent Governance
Unchecked AI deployment can create security gaps and shadow IT. We build unified multi-agent systems with strict Role-Based Access Control (RBAC). SaaS tenant data remains strictly segregated, and factory managers retain absolute approval authority over any AI-executed transaction.
Agent Drift Defense
AI agents degrade silently as user behavior shifts and data schemas evolve. We deploy continuous monitoring pipelines that detect accuracy drops at the agent level and trigger retraining before they reach your bottom line, so your agents stay reliable long after deployment.
What We Deliver
Services We Provide
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Multi-Agent Readiness Assessment
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Custom Multi-Agent AI Platform Development
- Agent Orchestration Layer. Our team will design your agent architecture using MCP-aligned tool access, A2A/ACP communication patterns, and Code Mode orchestration to ensure scalable, deterministic multi-agent behavior. You get a production-ready orchestration layer with defined delegation flows, recovery paths, and predictable control over agent interactions.
- Tooling Design. We will create tool interfaces and function schemas using MCP server patterns, strict permissioning, and isolated execution to define safe, reliable agent capabilities. Ideal for teams needing consistent behavior, you receive a validated tool catalog with clear boundaries, predictable call patterns, and minimized risk of uncontrolled agent actions.
- Agent Memory Management. Our team will implement short-term and long-term memory using structured state stores, shared context channels, and Code Mode sandboxing to avoid contextual issues. For teams that need dependable continuity, you get a stable memory model with predictable retrieval, reduced token overhead, and safer handling of sensitive operational data.
- Reliability engineering. We will apply guardrails, loop-detection logic, cost controls, and token-usage limits informed by common failure modes. By enforcing these constraints, you gain a hardened reliability layer that reduces runaway behavior, stabilizes execution paths, and keeps operational costs within expected ranges.
- Production Observability. Our team will deploy your platform with tracing, evaluation pipelines, and monitoring aligned to LangGraph-style state visibility and enterprise observability requirements. Through this instrumentation, you get actionable telemetry, clear performance baselines, and continuous insight into agent behavior across production workloads.
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Agentic Workflow Orchestration System
- Autonomous Task Planning. Our team will build autonomous planners that use MCP‑aligned tool discovery, Code Mode orchestration 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 sandboxed Code Mode scripts, retry logic and 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 hand‑offs, eliminate redundant steps and create measurable efficiency across departmental boundaries.
- Conditional Routing. We will design conditional routing using agent‑to‑agent delegation rules, structured state transitions and Code Mode branching to ensure safe, deterministic hand‑offs between autonomous agents. Made for complex environments, you get routing logic that prevents circular delegation, enforces clear ownership, and 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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Agentic Knowledge System
- Retriever Agent. Our team will build a Retriever Agent that uses multi-source retrieval, MCP-controlled tool access, and Code Mode discovery to pull only the most relevant enterprise knowledge. Anchored in precision, you get a retrieval layer that surfaces high-value documents, reduces noise, and consistently feeds downstream agents with clean, context-rich inputs.
- Critic Agent. We will implement a Critic Agent that applies relevance scoring, quality filtering, and hallucination-reduction logic informed by common failure modes. Built for teams that need dependable accuracy, you gain a filtering layer that removes weak evidence, stabilizes reasoning chains, and keeps downstream synthesis grounded in verified material.
- Synthesizer Agent. Our team will design a Synthesizer Agent that performs multi-document reasoning using structured context windows, Code Mode orchestration, and controlled tool calls to assemble coherent, defensible answers. Useful when you require consolidated insights; you get structured outputs that merge multiple sources, preserve nuance, and maintain traceability across complex knowledge sets.
- Validator Agent. We will create a validator agent that performs fact‑checking and source verification using MCP‑restricted access, isolated execution, and strict guardrails to prevent unverified claims from reaching users. Best for environments demanding trust, you receive validated responses with confirmed citations, reduced hallucination risk, and predictable quality across sensitive knowledge workflows.
- Knowledge Infrastructure. Our team will build hybrid retrieval infrastructure using hybrid retrieval mechanisms to support citation‑ready multi‑agent reasoning. Meant for organizations scaling knowledge automation, you get a durable retrieval backbone with consistent embeddings, structured relationships, and reliable grounding for every generated answer.
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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 using asynchronous multi-agent coordination, Code Mode pipelines, and controlled tool calls to support real-time stream analysis. You get 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 and Code Mode orchestration for unified decision-making. 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 multi‑modal 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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Enterprise Team Agents
- Research Agents. Our team will build research agents that use MCP‑restricted retrieval, Code Mode orchestration and multi‑document reasoning to gather, filter and synthesize information across enterprise sources. The outcome is a research layer that delivers clean evidence, structured summaries and dependable inputs your teams can trust for fast internal decision‑making.
- Analyst Agents. We will develop analyst agents that apply structured reasoning, controlled tool calls, and sandboxed data processing to generate insights without leaking sensitive information or overloading context windows. This gives you consistent analytical outputs, reduced manual effort, and clearer visibility into patterns that matter across key enterprise datasets.
- Project Manager Agent. Our team will create a project manager agent that coordinates tasks using A2A/ACP delegation, state tracking, and MCP-aligned updates across connected enterprise systems. The result is predictable coordination, timely status visibility, and fewer dropped hand‑offs across distributed teams working on complex internal initiatives.
- Executor Agents. We will implement executor agents that perform safe action‑taking through MCP‑controlled tools, strict permissioning and Code Mode scripts that isolate execution and prevent unintended system‑wide changes. You’ll reduce operational friction by automating routine actions while keeping every action auditable and governed.
- Shared Team Workspace. Our team will build a shared workspace that unifies agents and humans through structured state sharing, cross‑agent communication and observability aligned with enterprise governance. You’ll free up team capacity with a collaborative environment where agents coordinate transparently, surface context proactively and keep everyone aligned on current work.
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Autonomous Multi-Agent Operations Framework
- Specialized agent fleets. Our team will design specialized agent fleets using MCP‑restricted tool access, A2A/ACP delegation rules and Code Mode orchestration to ensure each unit operates within tightly defined boundaries. You get 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 using strict MCP permissioning, approval gates and structured exception routing to keep humans in meaningful control. Built for teams that require accountable autonomy, you get clear intervention points, predictable escalation flows and reduced risk of unreviewed high‑impact actions.
- Agent Improvement Loops. We will create self‑improvement loops using telemetry signals, performance traces and controlled feedback channels to help agents refine behavior without triggering degenerative sequences. Useful when you need continuous optimization, you gain adaptive agents that improve within guardrails, maintain stability and avoid runaway learning patterns.
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Governed Agent Deployment
- Agent Access Controls. Our team will implement least‑privilege access using MCP‑scoped tool exposure, strict permission boundaries and sandboxed Code Mode execution 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 using controlled data flows, VPC‑isolated execution and immutable decision‑logging patterns. You’ll cut compliance overhead with structured controls that reduce audit friction, clarify data‑handling boundaries and maintain verifiable adherence across sensitive workflows.
- Decision Traceability. Our team will generate immutable audit trails using MCP‑aware logging, structured state capture and Code Mode traceability 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.
- Prompt Injection Defense. We will harden your agent ecosystem using permission‑scoped tool calls, guarded context windows and loop‑limit protections that reduce exposure to prompt‑injection and adversarial manipulation. You’ll shorten the attack surface with defensive layers that constrain agent behavior, validate inputs and prevent malicious patterns from propagating across multi‑agent chains.
- Runaway Cost Controls. Our team will enforce budget caps, token‑usage limits and degenerative‑loop detection using telemetry signals and Code Mode orchestration to contain runaway execution. You’ll validate operational stability with predictable cost envelopes, controlled autonomy boundaries and early‑warning signals that prevent uncontrolled agent expansion.
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Multi-Agent Migration Program
- Data Platform Connectors. We will build connectors for key enterprise platforms using MCP servers, strict allow-lists, and isolated execution to safely bridge legacy platforms into multi-agent workflows. You gain reliable access paths that expose only the required functions, reduce integration risk, and preserve existing system investments.
- Event-Driven Integration. Our team will design an API and event-driven layer using A2A/ACP coordination and Code Mode orchestration to create secure, deterministic bridges between agents and enterprise systems. Driven by predictable communication, you get a unified integration fabric that supports async events, controlled delegation, and consistent behavior across distributed processes.
- Phased migration. We will run phased migrations using parallel-run patterns, telemetry signals, and controlled cutover steps to transition from manual processes to agent‑driven operations safely. Anchored in operational stability, you receive a migration plan that reduces disruption, validates behavior at each stage and ensures predictable adoption across teams.
- Adoption Enablement. Our team will support adoption through structured training, workflow mapping, and governance alignment informed by real multi-agent behavior patterns and containment-rate insights. Built for teams that need smooth onboarding, you get clear guidance, practical materials and confidence that employees can collaborate effectively with new autonomous systems.
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Industry-Specific Multi-Agent Solutions
- Supply Chain. Our team will build supply-chain agents using MCP-restricted tool access, A2A coordination, and Code Mode orchestration to manage core logistics tasks with predictable autonomy. You get 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 handle core revenue cycle tasks safely. You get consistent claim processing, less manual review, and higher throughput while maintaining HIPAA‑aligned data protections.
- Insurance Claims. We will build insurance-claims agents using structured retrieval, relevance filtering, and guarded decision flows to support 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, reduced stock volatility, and clearer pricing signals powered by agents that coordinate safely across large, fast-changing retail datasets.
AI
From AI-Assisted Speed to Release-Ready Wallet Engineering
Devox Software helped stabilize a self-custody wallet for launch by tightening mobile, backend, and QA work around transaction flows, secure recovery, and multi-network behavior.
Additional Info
Core Tech:
- Blockchain
- React Native
- Kotlin
- Swift
- TypeScript
- Node.js
- REST APIs
- SDK integrations
- Ethereum
- Polygon
Country:
Estonia
Logistics
Scalable, AI-Powered TMS to Drive Market Expansion for a Logistics Company
A logistics multi-module platform that helps manage parcel tracking, intelligent routing, barcode generation, and returns.
Additional Info
Core Tech:
- Node.js
- React.js
- PostgreSQL
- AWS Cloud Infrastructure
- NetworkX
Country:
United Kingdom
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
Minimal Viable AI: How to Integrate Small‐Scale AI Features into Existing Products
AI Solutions
Fintech
Web3
Scaling Trust: How AI-Augmented Architectures Accelerate Web3 Business Adoption
FAQ
Frequently Asked Questions
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Why not build this in-house?
Building a simple chatbot interface is straightforward; architecting autonomous multi-agent systems requires specialized orchestration expertise. Internal teams often encounter challenges with complex failure modes. Partnering with Devox Software closes that capability gap quickly, allowing your in-house engineers to stay focused on the core product while we implement proven AI frameworks.
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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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How do agents handle bad data?
If an API returns invalid data, a basic AI system may infer an answer and generate unreliable output. We engineer our agents with strict exception handling. If data schemas do not match or the agent’s confidence score falls below a defined threshold, execution stops and the case is routed for human review. The system does not make assumptions in critical business decisions.
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How do you control token costs?
Excess context is one of the largest hidden costs in AI. Passing large datasets back and forth to an LLM can become expensive quickly. We address this with a code-execution architecture. Instead of using the LLM as a data transport layer, our agents run lightweight Python scripts to process large volumes of data locally in a secure sandbox. This can reduce token consumption by more than 90 percent.
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How do you prevent unauthorized actions?
We follow the principle of least privilege through MCP gateways. Agents do not receive broad access to your systems; they are assigned explicit allow-lists and can use only the tools required for the task at hand. Any high-risk action, such as a database write or financial transfer, is configured to require human approval before execution.
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Will agents slow down our app?
Not if the system is designed correctly. We reduce unnecessary LLM round-trips by using sandboxed code execution for intensive data processing. An operation that might otherwise require six model interactions can often be completed in three or four, reducing latency and supporting responsive application performance.
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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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