AI Agent Development

What Is an AI Agent?

An AI agent perceives context, reasons about a goal, takes actions to achieve it, and adapts based on what it observes. This is meaningfully different from a basic chatbot or a rule-based automation. A well-built AI agent handles multi-step tasks, uses tools like APIs and databases, manages context across a conversation or workflow, and knows when to escalate to a human.

We build single-agent systems for focused tasks and multi-agent systems for complex workflows requiring multiple specialised agents collaborating. We also build copilots AI assistants embedded in your existing software that suggest actions, draft responses, and reduce cognitive load on your team.

Types of AI Agents We Build

Copilots

AI assistants embedded inside your existing tools suggesting actions, drafting content, summarising information, and making decisions faster without replacing the people making them.

Autonomous Agents

Agents that operate independently to complete multi-step tasks planning a sequence of actions, using tools, retrieving information, and adapting their approach based on what they encounter.

Multi-Agent Systems

Multiple specialised agents collaborating on complex workflows each responsible for a specific step, coordinated through a designed protocol. More capable for complex tasks, built carefully so they work reliably.Multiple specialised agents collaborating on complex workflows each responsible for a specific step, coordinated through a designed protocol. More capable for complex tasks, built carefully so they work reliably.

Our AI Agent Services

AI Agent Consulting & Strategy

Before building anything, we identify high-value use cases, assess feasibility, review your data and infrastructure, and develop a clear roadmap. We are honest about what AI agents can and cannot do our consulting helps you make an informed decision.

Use case identification and feasibility assessment

Agent architecture and design recommendations

Data and infrastructure readiness review

Implementation roadmap and cost-benefit analysis

Custom AI Agent Development

We design and build agents tailored to your specific requirements handling the full lifecycle from reasoning architecture and LLM integration through to memory management, tool configuration, and deployment.

LLM-powered task and workflow automation agents

Decision-making and recommendation agents

Research and information synthesis agents

Internal operations agents for HR, IT, and finance

Conversational AI Agent Development

We build multi-turn, context-aware conversational agents that go well beyond scripted chatbots understanding natural language, maintaining context, handling complex queries, and knowing when to hand over to a human.

Customer support and query resolution agents

Sales qualification and lead nurturing agents

Appointment scheduling and service booking agents

Internal HR, IT helpdesk, and knowledge base assistants

AI Chatbot Development

NLP and LLM-powered chatbots deployed on your website, mobile app, WhatsApp, Slack, or Teams integrated with your knowledge base, CRM, and backend systems so responses are accurate and relevant.

Customer-facing service and support chatbots

Multi-channel deployment web, WhatsApp, Slack, Teams

Intent recognition, entity extraction, and fallback handling

Chatbot analytics and conversation quality monitoring

AI Agent Integration

We integrate agents into your existing software ecosystem CRM, ERP, ticketing systems, and communication platforms using API architecture, microservices, and containerisation for reliable production deployment.

CRM, ERP, and enterprise system integration

Single-agent and multi-agent system design

Real-time data integration and event-driven agent triggers

Fine-Tuning, Optimisation & Monitoring

AI agents in production need to improve over time. We update prompt engineering, refine RAG pipelines, expand knowledge bases, and monitor performance continuously tracking task success rate, response quality, and drift.

Prompt engineering and systematic iteration

RAG pipeline and knowledge base optimisation

Real-time performance dashboards and drift detection

Latency and cost optimisation for production agents

Technology Stack

NLP & Conversational AI

spaCy • NLTK • Hugging Face Transformers • BERT • Rasa NLU • Dialogflow

Memory & State Management

Redis • Pinecone • Weaviate • Chroma • FAISS • SQLite

LLM Models

GPT-4, GPT-4o (OpenAI) • Claude (Anthropic) • Gemini (Google) • LLaMA 3, Mistral (open-source)

Agent Frameworks

LangChain • LlamaIndex • AutoGPT • CrewAI • AutoGen • OpenAI Assistants API • Microsoft Bot Framework

Deployment & Infrastructure

FastAPI • Docker • Kubernetes • REST APIs • WebSockets

Cloud Platforms

AWS SageMaker & Bedrock • Azure OpenAI Service • Google Cloud Vertex AI

Monitoring & Evaluation

LangSmith • PromptLayer • Weights & Biases • MLflow

Industries We Serve

Healthcare

Patient query handling, appointment scheduling, clinical documentation assistance, medication reminders, and administrative load reduction for clinical staff.

Finance & Fintech

Customer service agents for account queries, compliance monitoring, automated report generation, fraud signal processing, and internal operations assistants.

Retail & eCommerce

Order tracking and returns chatbots, sales qualification agents, product recommendation assistants, and inventory and supplier communication automation.

Logistics & Manufacturing

Predictive maintenance agents, operations monitoring, supply chain disruption alerts, delivery status query handling, and field operations support.

Education

Student query handling, administrative assistance, AI tutoring that adapts to individual learning patterns, and knowledge base agents for institutional staff.

Travel & Hospitality

Booking and reservation agents, itinerary management, multilingual customer support, loyalty programme query handling, and property management operations support.

How We Work

We define the agent’s goals, scope its capabilities, and identify the data and systems it needs to access.

We design the reasoning architecture LLM selection, memory management, tool access, failure handling, and system integration.

We build iteratively with regular evaluation checkpoints. Prompt engineering receives as much attention as the code.

We test against defined quality metrics task success rate, accuracy, latency, hallucination rate, and edge case handling.

We deploy to your infrastructure with monitoring, alerting, and logging configured from day one.

We monitor performance continuously, identify improvements, and provide structured post-deployment maintenance.

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Why Web Chip Armor

Genuine AI agent expertise

Our developers understand LLMs, agent architectures, NLP, and the practical challenges of deploying AI reliably in production. Not generalists learning on your project.

Production-grade engineering

Clean, documented, testable code with proper architecture. The agents we build are maintainable not dependent on the original team to keep running.

Evaluation built in from day one

We define success metrics before we build, measure throughout, and deliver agents with monitoring in place. You know whether it's working.

Agile delivery with real visibility

Structured sprints, regular deliverables, and clear communication. You see working software at each stage no large untested system at the end of a long engagement.

NDA signed before any project discussion

Your business requirements, data, and code remain strictly confidential throughout and after the engagement.

Measurable results, not just deployment

The goal is not a running agent. The goal is an agent that demonstrably improves the metric it was built to improve.

Frequently Asked Questions

What is the difference between an AI agent and a chatbot?

A basic chatbot follows scripted flows and handles expected inputs. An AI agent understands natural language, maintains context, takes actions like querying databases or calling APIs, makes decisions based on context, and handles unexpected inputs gracefully. An agent manages complex, multi-step interactions in a way a scripted chatbot cannot.

Can you integrate AI agents with our existing systems?

Yes. CRM, ERP, ticketing systems, communication platforms, and data sources integration is a core part of what we do. An isolated agent that cannot access your actual data has limited value. We use REST APIs, WebSockets, and microservices patterns to ensure reliable integration within your existing infrastructure.

How do you handle data security and privacy?

Encryption in transit and at rest, strict access controls, audit logging for agent actions, and privacy-by-design architecture. For sensitive data we recommend Azure OpenAI Service or AWS Bedrock for enterprise privacy guarantees, or self-hosted open-source models in your own infrastructure.

How long does an AI agent project take?

A focused customer support chatbot typically takes six to ten weeks. A multi-agent workflow with complex integrations may take three to six months. We give you a realistic timeline after a discovery conversation where we understand your requirements properly.

What does it cost?

It depends on the complexity of the agent, the number of integrations, and the engagement model. We provide a detailed, transparent estimate after the discovery phase. We offer both fixed-price project and dedicated team models.

Do you provide support after the agent is deployed?

Yes. Ongoing maintenance is a core part of our offering model and API updates, knowledge base maintenance, performance monitoring, and capability improvements. AI agents are not fire-and-forget deployments.

Ready to Build Your AI Agent?

If you have a business challenge that AI agents could help address automating a workflow, handling customer interactions at scale, supporting your internal teams, or building intelligent automation into your product we’d like to hear about it.