Agentic AI Development Services

Build production-ready agentic AI systems with CHI Software: custom AI agents, RAG, multi-agent workflows, enterprise integrations, security, and observability.

Our Growing Network of Clients

Production-Ready Agentic AI Systems Built Into Your Architecture

Building a demo is easy. The real challenge is making AI agents reliable in production — integrated with real systems, under high load, and inside existing architecture.

We run 10+ AI agents in production across Sales, Finance, Legal, HR, and Marketing, reflecting a production-first approach rather than experimental use.

The 2025 DORA report confirms a common risk: many teams only discover AI-generated issues after deployment, often due to gaps in observability, CI/CD, and review processes.

That’s why production-ready agentic systems must be designed with reliability, testing, and observability from the start.

What Agentic AI Development Means in Practice

Beyond Chatbots: AI Agents That Plan, Use Tools, and Complete Tasks

The difference is architectural: agents maintain state, use tools, evaluate intermediate results, and adjust their workflow. They can complete multi-step processes like checking a CRM, drafting a response, sending an email, and logging outcomes with minimal human intervention.

How AI Agents Work Inside Existing Business Systems

In production, agents don’t run alone — they interact with APIs, databases, and workflows, and hand off tasks to humans or other agents. Most failures happen at the integration level (auth, errors, rate limits, data quality), not in the agent logic itself.

Why Architecture, Security, and Observability Matter from Day One

Without observability, agents can’t be debugged, tuned, or audited. Regulations like the EU AI Act also require audit trails, explainability, and human oversight for high-risk systems, making these controls essential from day one.

Where Human Control Fits Into Agentic AI Workflows

The best agentic systems are designed with deliberate human-in-the-loop checkpoints — approval gates for high-stakes decisions, escalation paths when the agent’s confidence is low, and clear override mechanisms.

Agent Maturity Levels For Enterprise AI

One of the most common mistakes companies make is trying to skip straight to a complex multi-agent environment before the delivery infrastructure is ready to support it. The right maturity level depends on your platform readiness:

L1 Foundational Agents
RAG bots, FAQ assistants, classifiers. These handle structured lookups, answer questions from a knowledge base, or categorize inputs. Fast to deploy, low integration complexity, and a good entry point if your team is new to agentic AI.
L2 Tool-Use Agents
Agents that call external tools, like APIs, databases, and search indexes, to complete specific tasks. CRM automation and live data queries fit here. Requires solid integration design and error handling, but the scope stays well-defined.
L3 Autonomous Agents
Multi-step planning, long-running sessions, adaptive decision making. Voice AI agents, IT support agents, and research assistants typically operate at this level. Require observability infrastructure and output evaluation to run safely.
L4 Multi-Agent Systems
An orchestrator managing a set of specialized sub-agents, each handling a different part of a complex workflow. Customer support pipelines and back-office automation are common use cases. Governance, shared state, and inter-agent communication all need careful architecture here.
L5 Agentic Enterprise
Persistent memory, full audit trails, governance controls, cost management, AI infrastructure woven across the entire organization. This is the full maturity of enterprise-grade agentic AI systems — the end state most companies are working toward.

How to Choose the Right Agent Maturity Level for Your Business

Start with an honest look at your existing delivery pipeline. If you don’t have reliable observability, rollback coverage, or integration testing today, adding autonomous agents will make those gaps more painful. A platform readiness assessment before the build phase protects both the timeline and the investment.

What We Build with Agentic AI

RAG and Knowledge Systems Retrieval-augmented generation (RAG) systems connect AI agents to proprietary business knowledge, internal documentation, databases, and structured enterprise data. These solutions improve information retrieval, accelerate internal workflows, and provide more accurate, context-aware responses while maintaining governance and traceability across complex data environments.
Voice AI Agents AI-powered voice agents help automate customer support, scheduling, internal operations, and service workflows. Integrated with telephony infrastructure and backend business systems, these solutions support real-time conversations, multilingual communication, and workflow automation while reducing repetitive operational workload.
Document Intelligence Agents Document intelligence systems transform unstructured files into structured, actionable data using OCR, NLP, and classification technologies. These agents automate document extraction, validation, processing, and routing workflows across industries where large-scale document handling is part of daily operations.
Human-in-the-Loop Agent Workflows Not every workflow should operate autonomously. In many enterprise environments, human oversight remains essential for compliance, governance, and operational control. Agentic AI workflows can include structured approval checkpoints, escalation paths, and review mechanisms that help organizations maintain transparency while improving execution speed and operational efficiency.
Secure and Observable AI Agents Enterprise-grade AI systems require governance, monitoring, and visibility from the start. Secure and observable agentic AI architectures include tracing, compliance logging, PII filtering, monitoring, cost tracking, and evaluation frameworks that help teams understand agent behavior, identify issues early, and maintain reliable performance in production environments.
Enterprise Workflow Automation Agents AI agents can operate across multiple enterprise systems and coordinate workflows between tools, teams, and data sources. These systems support automation across CRM, ERP, ITSM, and internal business platforms while improving execution speed, reducing manual effort, and enabling scalable cross-functional operations across the organization.

DORA 2025 found that 60 percent of engineering teams discovered AI-generated errors only after deployment. Building agentic AI that works in production requires CI/CD, structured code review, and observability from day one, not as an afterthought.

If your team is moving from a working prototype toward a production system, that is exactly where we start.

Review Our Production Delivery Approach

When Companies Need Agentic AI Development Services

Manual Workflows Slow Down Teams

High-volume repetitive work, document processing, data entry, report generation, ticket routing, absorbs capacity that should go toward building product. Tasks that previously required hours of manual coordination can often be completed within minutes through AI-powered workflow automation and intelligent task orchestration.

Existing Automation Cannot Handle Contextual Decisions

Rule-based automation breaks the moment something changes — a new document format, an unexpected approval path, an edge case the rules didn’t cover. AI agents that reason about intermediate results and adapt their path replace brittle scripts with systems that handle real-world variability without breaking.

AI Pilots Do Not Move Into Production

85% of AI projects never reach production without a clear architecture and process map. The problem usually isn’t the model; it’s that the pilot was built without the integration design, observability, and deployment infrastructure needed to actually ship. GitClear’s 2025 analysis of AI-assisted codebases shows why: without review discipline, the surrounding codebase accumulates technical debt faster than any other development approach.

Business Systems Need Smarter Cross-Tool Automation

CRMs, ERPs, ticketing systems, and data pipelines weren’t designed to share context. Agents that operate across multiple tools require a careful integration architecture and coordination logic — work that falls outside the bandwidth of most internal teams while they deliver on their roadmap.

DORA 2025 confirms that AI amplifies existing engineering strengths and weaknesses. Teams without a strong DevOps foundation see more instability, not less, when they add agentic AI. If your current delivery pipeline has gaps in observability, rollback coverage, or integration testing, those gaps will show up faster once agents are running in production.

We assess the platform before any agent build starts.

Get a Platform Readiness Check Before You Build

Our Agentic AI Development Services

Agentic AI Architecture Design

System design for agent orchestration, tool use, memory, integration layers, security controls, and observability. We also do architecture reviews for teams that have a working prototype and need a credible path to production.

Custom AI Agent Development

Purpose-built agents for specific business processes, integrated with your existing systems, shipped with evaluation frameworks, logging, and monitoring already in place. Fixed Price, T&M, or BOT engagement model — whichever matches your delivery structure.

Multi-Agent System Development

Design and delivery of coordinated multi-agent environments: orchestrator-plus-subagent architectures, shared state management, task delegation, inter-agent communication, and governance controls for complex workflows.

Enterprise Agentic AI Implementation

Full implementation inside enterprise architecture — cloud infrastructure on AWS, Azure, or GCP, security and compliance configuration, integration with CRM, ERP, and data platforms, production deployment, and ongoing monitoring. All infrastructure is documented in your repository.

Technology Stack for Agentic AI Design and Development

Generative AI Tools
• LLM: GPT-4, Anthropic Claude, Google Gemini Pro, Misral, Mixtral, Grok, Llama, Gemma, Groq, AWS bedrock (foundation models)
• Frameworks: Langchina, Langgraph, LlamaIndex, DSPy, Llama Hub, Perplexity, Ollama
• AI Agents: Langchain agents, CrewAI 2.0 , AutoGen
Deep Learning Frameworks
• PyTorch
• Caffe2
• NVCaffe
• Chainer
• Theano
• MXNet
Modules / Toolkits
• Kurento
• Microsoft Cognitive Toolkit
• Core ML
Libraries
• OpenNN
• TensorFlow
• Sonnet
• TF-Slim
• Tensor2Tensor
• Neuroph
Frontend
• TypeScript
• AngularJS
• Next.js
Backend
• Node.js
• Python
• R
Python Frameworks
• FastAPI
• Flask
• Django
Cloud Providers
• AWS
• Microsoft Azure
• Google Cloud
Image Classification Models
• VGG16
• ResNet-50
• Inception-v3
• EfficientNet
Embeddings
• OpenAI
• HuggingFace (BERT, RoBERTa for text, CLIP for images, Wav2Vec2)
• textembedding-gecko by Vertex AI
Algorithms
•Supervised/Unsupervised Learning
• Clustering
• Metric Learning
• Few Shot Learning
Neural Networks
• CNN
• RNN
• Representation Learning
• Manifold Learning
• Variational Autoencoders
• Bayesian Network
• Autoregressive Networks

High-risk AI systems in healthcare, finance, education, and HR require audit trails, explainability, and human-in-the-loop controls under EU AI Act. Penalties reach up to 35 million euros or 7 percent of global revenue.

We design governance and observability into every agent architecture from the first sprint, not as a compliance retrofit at the end.

See How We Build Governance Into Agentic AI

How We Build Production-Ready Agentic AI Systems

We ran this process on ourselves before we ran it for anyone else. Our own company went from manual workflows to 10 production AI agents across five departments. Here’s how the delivery works:

Discover and Define the Right Agent Maturity Level
Week one is stakeholder interviews, workflow mapping, and bottleneck identification. Week two is architecture and maturity assessment — system structure, integration points, and actual AI readiness constraints. The output is a phased roadmap with KPIs, measurable success criteria, and a starting point aligned with your actual platform readiness.
Design Architecture, Integrations, and Guardrails
Agent orchestration patterns, tool use specifications, memory and state design, integration contracts, security controls, and observability requirements are all defined here. Human oversight mechanisms and escalation paths are part of this phase. We don’t design those later.
Build, Test, Deploy, and Optimize
Incremental delivery with rollback criteria defined at each phase. Agent behavior evaluation frameworks run alongside functional testing. Integration tests cover failure paths, not just happy paths. Monitoring is active before each phase goes live. GitClear’s finding that code duplication increased eightfold without review discipline is exactly why our structured code review is non-negotiable at every sprint.
Monitor Performance, Costs, and Agent Behavior
Post-launch tracking covers output quality, latency, token costs, integration error rates, and anomaly detection. Behavior drift — where an agent’s outputs degrade over time — is tracked and automatically flagged. You have full visibility into what your agents are doing and what they’re costing.

GitClear 2025 analysis of 211 million lines of code found that AI-assisted development increased code duplication eightfold in 2024 when review discipline was absent. Our agentic AI delivery includes structured code review, evaluation frameworks for agent behavior, and incremental rollout with defined rollback criteria.

Every phase ships with monitoring in place before the next phase starts.

Talk to Our Engineering Team About Your Agent Build

Business Impact of Agentic AI Systems

Faster time to production

AI accelerates coding, reviews, and documentation, helping teams ship faster—provided CI/CD, testing, and architecture are already strong.

Lower manual workload

Routine engineering tasks are increasingly automated, freeing engineers to focus on design, integration, and system-level decisions.

Reliability depends on governance

GitClear research shows AI can increase rework and code duplication if not properly controlled, making review and quality systems more important, not less.

Better visibility and auditability

Agentic workflows improve traceability of changes, decisions, and engineering activity, enabling better operational insight.

Risk of technical debt growth

Without strong engineering discipline, AI-driven speed can outpace quality controls, leading to higher long-term maintenance costs.

What Our Clients Say

CHI Software Team were involved in the whole SDLC, including design development, implementation and maintenance of new and/or existing application systems. We had an excellent experience working with them so far.

Sylvain Thiebaut

CTO at BetterTradeOffPteLtd

CHI Software played a crucial role in optimizing our mobile application, ensuring seamless functionality across iOS and Android platforms. Initially brought in for a short-term fix, their expertise quickly became indispensable as they resolved critical publishing issues, modernized outdated libraries, and enhanced navigation. Their proactive approach in identifying and fixing hidden flaws significantly improved app stability and usability. CHI Software’s adaptability, deep technical knowledge, and ability to streamline a previously fragmented system have made them a trusted technology partner in this project’s success.

Nick Valstar

Lead Data Engineer at Cefetra Group B.V.

On our request, CHI Software developed a basic AI Assistant, designed to enhance our business operations with a fine-tuned prompting feature. Built with Azure enterprise-grade security and OpenAI technology, this solution is poised to improve customer engagement and operational efficiency as we prepare to launch.

Having been our trusted partner for previous projects, CHI Software has now solidified its position as an even more indispensable ally, delivering innovation and expertise that exceed expectations.

Eva-Valérie Gfrerer

Founder & CEO at Morphais

Media Markt worked with CHI Software for over 4 years. During this time the CHI team made important contributions to our e-commerce projects and to one of our core proprietary technologies in the area of distributed data synchronization. Alongside outstanding technical skills, these engineers have always been highly reliable in terms of quality and deadlines. Taking into consideration my experience, I would highly recommend working with the CHISW engineering team.

Bulat Rakhimberdiev

Solution Manager at Media Markt

Collaborators are full of well-being. The whole team is doing their best for the good development of projects. Also, I appreciate the general concepts of how CHI Software does business with its customers. It has been a very efficient collaboration from the very beginning. I believe we have a good potential to work in the future.

David Ortiz

Founder at NHI Colombia

Although the development process is ongoing, the travel agency notes that the CHI Software team is attentive and receptive to ideas. The client also commends the team's communication style and availability, as well as their business-oriented approach to the project.

Pavlo Golovnenko

CEO at Piligrim

The CHI Software team were able to implement several applications in the app that were in accordance with the requirements of the client. This allowed them to speed up and simplify the interface and make it more comfortable to use. The team's workflow was efficient.

Andriy Fedorenko

COO at Soft Industry Alliance

Why Choose CHI Software for Agentic AI Development

We did it ourselves first.
Our entire company runs on Claude Code. We deployed 10 AI agents across five departments before offering this to anyone else. Every number we cite — the 95% reduction in sales deck prep, the 30-minute invoice reconciliation — comes from measuring our own operations.
We have real AI depth.
PhD researchers in AI, NLP, and Machine Learning on staff, published in ACL and PLoS. 30+ certifications across Google Cloud, AWS, and Deep Learning.AI.
We have production proof across real industries.
Banyan Infrastructure, Blackstone, Cache Invest, Imagine Learning, Trapelo Health — clients in FinTech, HealthTech, and EdTech, who trusted us with their core operations.

CHI holds ISO 27001 and ISO 9001 certifications and delivers on AWS, Azure, and GCP without binding clients to a proprietary toolchain. All infrastructure is documented in the client repository. If your team has internal AI engineers and needs specific expertise for agent orchestration, MLOps, or enterprise integrations, we can extend the team without a full managed engagement.

Discuss Team Extension or Full Build Options

FAQs About Agentic AI Development Services

How is agentic AI different from a regular AI chatbot?

A regular chatbot mainly responds to user prompts and follows predefined conversation flows. Agentic AI goes further — it can plan actions, make decisions, and execute multi-step tasks autonomously across systems and tools, not just provide answers.
Can agentic AI systems integrate with our CRM, ERP, databases, and internal tools?

Yes. Agentic AI systems are designed to integrate with your existing tech stack. They can connect to CRMs, ERPs, internal databases, APIs, and other enterprise tools to retrieve data, update records, and trigger workflows securely.
How do you keep AI agents secure, auditable, and controlled?

Security is built into the architecture. This includes role-based access, strict permissions for tools and actions, full logging of all agent activity, and human-in-the-loop approval for sensitive or high-risk operations to ensure control and transparency.
How long does it take to build an agentic AI system?

It depends on complexity and integrations. Simple agents can be built in a few weeks, while more advanced enterprise-grade systems with multiple integrations, workflows, and guardrails typically take a few months to design, implement, and deploy.

Start your AI journey right here!

Contact our AI team to build a roadmap for your project.

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