AI Agent Development Company
Build autonomous AI agents and LLM-powered agents that research, decide, and act on your behalf — 24/7, without manual intervention. Our AI automation services cover everything from single-task agents to complex multi-agent pipelines.
I design and deploy production-grade systems using LangChain, CrewAI, and Claude API. Whether you need a standalone workflow agent or a pipeline where specialised LLM agents collaborate in parallel, every build ships with observability, retries, and graceful error handling. These can also be combined with machine learning solutions or data science consulting for end-to-end intelligence pipelines.
Unlike generic AI demos, these systems are built for real production environments with reliability, monitoring, and scalability in mind.
Multi-Agent Architecture
117+
Projects Delivered
100%
Job Success Score
Top 3%
Upwork Globally
24h
Response Time
Understanding AI Agents
What Is AI Agent Development?
AI agents go beyond question-answering. They perceive a goal, plan a sequence of actions, use tools to execute those actions, observe the results, and iterate until the goal is achieved — all without a human steering every step.
New to AI agents? Think of it this way: if ChatGPT answers your questions, an AI agent is that same intelligence — but one that can also open your browser, do the research, send the email, update your CRM, and report back when it's done. It acts, not just responds. Many of our clients pair AI agents with RAG & LLM applications for even richer automation.
Traditional Chatbot
- ○Responds to messages
- ○Single turn
- ○No external tools
- ○Needs human input
Single AI Agent
- ✓Takes autonomous actions
- ✓Multi-step reasoning
- ✓Uses external tools
- ✓Handles one task end-to-end
Multi-Agent System
- ✓Parallel specialised agents
- ✓Orchestrator + sub-agents
- ✓Complex pipelines
- ✓Production-grade reliability
Is This Right for You?
When Do You Need AI Agents?
AI agents deliver the most value when human time is the bottleneck. Here are the clearest signals that autonomous AI agents are the right solution.
Repetitive manual workflows
Your team does the same multi-step task hundreds of times — copy data, send emails, update records. An agent does it in seconds, every time.
Multi-step business processes
Your workflow involves 3+ decisions or handoffs between tools (CRM → email → spreadsheet → Slack). Agents chain these steps without human glue.
Data-heavy decision making
You need to pull data from multiple sources, analyse it, and act on it — daily or in real time. Agents do this continuously without analyst bottlenecks.
Need to scale without hiring
You want to 10× output without 10× headcount. AI automation services let you scale research, outreach, or support capacity at near-zero marginal cost.
Need 24/7 automation
Your process can't wait for business hours — customer queries, data ingestion, or monitoring tasks that need to run overnight or across time zones.
Human error is costing you
Copy-paste mistakes, missed follow-ups, inconsistent formatting. Autonomous AI agents apply the same logic every single time — no fatigue, no shortcuts.
Applications
What Can AI Agents Do For Your Business?
Any repetitive, research-heavy, or multi-step business process is a candidate for automation. Here are the most common use cases I build.
Customer Support Automation
Agents that resolve tickets, escalate edge cases, and handle refunds autonomously — 24/7 with zero human oversight for routine queries.
Research & Intelligence Agents
Agents that crawl the web, aggregate data from multiple sources, and synthesise structured reports — in minutes, not days.
Content Generation Pipelines
Multi-agent pipelines that research, draft, review, and publish content — blog posts, product descriptions, social media — autonomously.
Data Extraction & Enrichment
Agents that scrape, clean, structure, and enrich data from websites, PDFs, and APIs — feeding clean records directly into your database.
Internal Knowledge-Base Q&A
Agents connected to your company docs, Notion, Confluence, or SharePoint — answering employee questions instantly with source citations.
Sales Outreach Automation
Agents that research prospects, personalise outreach emails, schedule follow-ups, and update your CRM — without a human SDR.
Competitive Intelligence
Agents that monitor competitor websites, pricing, job postings, and news — delivering weekly briefings without manual effort.
Code Review & Documentation
Agents that review pull requests, flag issues, suggest improvements, and auto-generate technical documentation from your codebase.
Who We Serve
Industries Served
AI agents deliver the highest ROI in information-intensive industries where research, writing, and decision-making happen at scale.
E-commerce
Product listing, customer service, returns
Healthcare
Patient intake, scheduling, report analysis
Finance
Research, compliance checks, report generation
Legal
Document review, case research, contract analysis
SaaS
Onboarding, support, usage analysis
Media
Content creation, curation, distribution
Manufacturing
Supply chain monitoring, defect reporting
Consulting
Research synthesis, proposal generation
How We Work
The AI Agent Development Process
Every agent I build follows the same battle-tested process — from whiteboard to production deployment.
Discovery & Architecture
We map your business process end-to-end: inputs, outputs, decision points, and edge cases. I design the agent architecture — how many agents, how they communicate, and what tools they need.
Prompt Engineering & Agent Design
Each agent gets a precise system prompt, clear task scope, and failure-handling logic. This is where most projects fail — vague prompts lead to unpredictable agents. I get this right.
Tool Integration & Testing
Agents are connected to your APIs, databases, search tools, and external services. I run adversarial testing — intentionally feeding edge cases to expose and fix failure modes before production.
Production Deployment
I ship every agent with retries, logging, observability, and rate-limit handling. Every action is traceable — you can see exactly what each agent did, when, and why.
Monitoring & Iteration
Post-launch, I monitor agent performance, catch regressions, and tune prompts based on real production data. Agents improve over time — not just at launch.
Why getyoteam
Why Work With Us?
Businesses in the USA, Europe, and Australia choose getyoteam for one reason: AI automation services that work reliably in production — not just in demos.
Top Rated Plus on Upwork
Independently verified Top 3% globally — 100% Job Success Score across 117+ projects. Not self-reported, not inflated. Real client outcomes.
USA · Europe · Australia
I work across time zones and am available for calls between EST and AEST. Most clients are based in the US, UK, and Australia — scheduling is never a barrier.
Production-First, Always
Every LLM agent ships with retries, observability, and error handling built in from day one — not patched on after launch. What you see in the demo is what runs in production.
Fast, Predictable Delivery
Most proof-of-concept AI automation builds complete in under a week. Full production multi-agent systems typically ship in 2–4 weeks with a clear milestone plan.
Direct Access, No Middlemen
You work directly with Kumar Katariya — not an account manager. I design, build, and deliver every system personally, or with my trusted team under my direct oversight.
30-Day Post-Launch Support
Every engagement includes a 30-day support window after deployment. AI agents need real-world tuning — I stay available until your system runs exactly as expected.
Technology
Tech Stack for AI Agents
Production-grade tools chosen for reliability, scalability, and ecosystem support — not hype.
Orchestration
LangChain and CrewAI for agent coordination, memory, and tool routing.
Observability
Langfuse traces every agent decision — full auditability in production.
Deployment
FastAPI + Docker on AWS Lambda or any cloud — scale from 0 to thousands of runs per day.
Proven Results
What Clients Achieved
RAG Document Intelligence Agent
The Problem
A knowledge-intensive team was spending hours manually searching dense PDF reports and contracts. Standard search found keywords but missed context — they needed natural-language answers with cited sources, instantly.
The Solution
Built a 6-step RAG pipeline using LangChain + ChromaDB + Gemini 1.5 Flash. Documents are chunked, embedded, and stored as vectors. At query time, the top relevant chunks are retrieved and passed to the LLM with a strict grounding instruction — no guessing from training data.
The Results
80%+
Faster research
Zero
Hallucinations
6
Pipeline steps
Free
Hosting cost
Outbound Sales Automation Agent
Built a multi-step sales agent that researches prospects, personalises outreach emails using company context scraped from their website, schedules follow-ups, and logs every interaction to the client's CRM — replacing 3 hours of manual SDR work per day with a fully autonomous LLM-powered workflow.
“Kumar acted with utmost professionalism and skill, working tirelessly to complete the project according to my standards. Highly recommended for any AI or ML project.”
Erika Shapiro
CEO, Study Song LLC
“Kumar and his team did a wonderful job. I now consider them an extension of my team. Their expertise in AI and attention to detail is outstanding.”
Zhanna Shekhtmeyster
Founder, ABC Observe
Common Questions
Frequently Asked Questions
What exactly is an AI agent, and how is it different from a chatbot?
A chatbot responds to messages. An AI agent takes actions. An agent can browse the web, write and execute code, call APIs, update databases, send emails, and make decisions — all autonomously, without a human in the loop. A chatbot is reactive; an agent is proactive.
How long does it take to build a custom AI agent system?
A single-agent proof-of-concept can be ready in 3–5 days. A production-grade multi-agent system with tool integrations, observability, and error-handling typically takes 2–6 weeks depending on complexity. I provide a detailed timeline after the discovery call.
What is the difference between a single agent and a multi-agent system?
A single agent handles tasks sequentially — one instruction at a time. A multi-agent system uses specialised agents working in parallel: an orchestrator breaks down the goal, sub-agents handle specific subtasks (research, writing, validation), and results are merged. Multi-agent systems are faster and more reliable for complex workflows.
Can AI agents connect to my existing databases, APIs, and internal tools?
Yes — this is a core part of what I build. Agents can query SQL/NoSQL databases, call REST APIs, read from Google Sheets, write to Notion, post to Slack, and integrate with any system that has an API. Custom integrations are built case-by-case.
How do you prevent the agent from making mistakes or taking wrong actions?
Production-grade agents need guardrails: input validation before each action, output verification after each step, human-in-the-loop checkpoints for irreversible actions, retry logic for transient failures, and full observability so every decision is logged and auditable. I build all of these by default.
What ongoing support do you provide after the agent is deployed?
I offer a 30-day post-launch support window included in every engagement. After that, I offer monthly retainers for monitoring, prompt tuning, and feature additions. Most clients find that agents need tuning in the first 30–60 days as real-world data surfaces edge cases that testing missed.
Stop Doing It Manually —
Let AI Agents Handle It for You
Describe the repetitive process costing your team the most time. I will respond within 24 hours with a proposed agent architecture, estimated timeline, and a plain-English explanation of how it works — no commitment, no jargon.
Trusted by businesses in the USA, UK, Europe & Australia · Top Rated Plus · 100% Job Success