Agents that finish the task, not just answer it
Hire an AI agent developer who builds systems that actually run autonomously
Custom AI agent solutions include autonomous task agents, multi-agent pipelines, LLM-powered workflow automation, and RAG-enhanced decision systems for real business environments, scoped against your actual process, not adapted from a generic template.
Available now, projects start within 48 hours, NDA before any data moves
Plain answer
What is an AI agent?
An AI agent is an autonomous software system that perceives its environment, sets a goal, selects the right tools, executes multi-step tasks, and iterates until the objective is complete. Unlike a standard chatbot that responds to one prompt and stops, an AI agent maintains memory across steps, breaks complex goals into sub-tasks, calls external APIs and databases, and produces a final result with minimal human input.
Businesses use custom AI agent solutions to automate research workflows, handle customer support end to end, run data analysis pipelines, coordinate between internal systems, and execute outreach sequences, reducing the manual overhead on repetitive knowledge-work processes. Common AI agent frameworks include LangChain, LangGraph, CrewAI, and AutoGen. The practical difference from a chatbot: a chatbot answers a question, an AI agent completes a task.
Why hire freelance
Why hire an AI agent development freelancer instead of an agency?
Agency AI agent development teams are built for enterprise RFP processes. They come with account managers, sprint ceremonies, and developer rotation. If your project needs one experienced AI agent developer who owns the architecture and ships it, a freelancer is the faster and more cost-effective path.
| Factor | Freelancer (Shreyans) | Agency |
|---|---|---|
| Who builds it | The developer you hire | Whoever is allocated |
| Communication | Direct, same-day response | Account manager relay |
| Accountability | Single owner, start to finish | Spread across a team |
| Cost | No markup layer | 2x to 3x overhead |
| Track record | 100% Upwork Job Success, verifiable | Team-level claims only |
| Agent frameworks | LangChain, CrewAI, AutoGen, LangGraph | Varies by assignment |
| Ramp-up time | Days | Weeks of onboarding |
Run the numbers first
What manual task handling is costing you
Tickets, research requests, outreach, whatever your team is completing by hand from start to finish. Move the sliders to match your volume.
Estimate only, based on delivered projects and the measurable-outcome ranges shown further down this page. The remaining share still escalates to a person, by design, for anything outside the agent's defined scope. Your actual completion rate is confirmed against real workflow data in the discovery phase.
What I build
Custom AI agent solutions and development services
Each service below is a complete delivery, not a component. Custom AI agent solutions are scoped against your actual business process, not adapted from a generic template. Violet tags are the more advanced, multi-system builds. Amber is the ongoing operations layer.
Autonomous task agents
Goal-directed agents that complete multi-step tasks without human intervention at each step: automated research agents, document processing agents, compliance monitoring agents, and data extraction pipelines. Built on LangChain and LangGraph with full tool-use, memory, and error-recovery loops.
- Task-oriented autonomous agents
- Multi-step reasoning and execution
- Secure and scalable architectures
Multi-agent systems
Collaborative agent networks where multiple specialised agents divide work, communicate, and coordinate to solve workflows too large or varied for a single agent. Frameworks used: CrewAI, AutoGen, LangGraph.
- Agent-to-agent communication
- Distributed task execution
- Intelligent coordination
Conversational AI agents
Context-aware conversational agents that maintain memory across sessions, handle interruptions, and escalate to human operators when needed. Distinct from basic chatbots in that they reason over conversation history and take actions in connected systems.
- AI chat and voice agents
- Context-aware conversations
- Omnichannel deployment
LLM-powered workflow automation
Automate knowledge-work processes using agents powered by GPT-4, Claude, Gemini, and open-source LLMs. RAG pipelines provide agents with access to your private data without fine-tuning.
- Tool-using AI agents
- Memory and reasoning capabilities
- Fine-tuned LLM integration
AI agent integration and API connectivity
Connect AI agents to your existing stack: CRMs, ERPs, internal APIs, cloud storage, web browsers, and third-party data sources. Includes authentication, rate-limit handling, structured output parsing, and error handling.
- Business process automation
- API and system integrations
- Reduced operational costs
Agent monitoring, evaluation, and optimisation
Production agents require ongoing evaluation: LLM evaluation frameworks, monitoring agent accuracy and latency, detecting output drift, and implementing retraining or prompt-update cycles. Standard on all monthly retainer contracts.
- Performance tracking
- Accuracy and reliability tuning
- Ongoing improvements
Not sure which one you need?
A 30-minute discovery call clarifies scope before anything is billed.
Talk it throughBuild stack
AI agent frameworks and technology stack
Framework selection depends on the agent architecture. Here is how each framework is used in practice.
| Framework / tool | What it is used for | Best suited to |
|---|---|---|
| LangChain | Tool-using agents, chain-of-thought orchestration, RAG pipelines | Single-agent workflows with external tool calls |
| LangGraph | Stateful multi-step agents with branching, loops, and human-in-the-loop | Complex workflows needing full control flow |
| CrewAI | Collaborative multi-agent systems with role-based task delegation | Parallel task execution across specialised agents |
| AutoGen | Conversational multi-agent frameworks with code execution | Research agents, coding assistants, agentic pair-programming |
| OpenAI API | GPT-4o as the reasoning core for tool-using and function-calling agents | High-reasoning tasks, structured output generation |
| Anthropic API | Claude as an alternative LLM core with a large context window | Document-heavy agents, long-context reasoning tasks |
| FastAPI | REST API layer wrapping deployed agents for system integration | Production deployment and external API access |
| Docker / AWS / GCP | Container deployment, scalable hosting, managed inference | Production infrastructure for live agent systems |
The idea behind the demo above
What is the difference between an AI agent and a chatbot?
A chatbot receives a message and returns a response. An AI agent receives a goal and takes a sequence of actions to complete it. The key differences are tool use, memory, and multi-step reasoning. A chatbot cannot call an external API, query a database, write a file, or iterate on its own output. An AI agent can do all of these. In practical terms, a chatbot can answer a customer's question, while an AI agent can receive a customer complaint, query the CRM, check order status, draft a resolution, and update the ticket, without a human touching it at each step.
When the answer is the whole job
- Simple FAQ or knowledge lookup with no system-of-record change needed
- Low-stakes queries where a wrong answer costs little
- A single-turn interaction is genuinely all the user wants
When the task needs to actually get done
- The task requires querying or updating a system of record
- Multiple steps or tools are needed before the job is complete
- You want the outcome delivered without a human following up
The demo above runs the same three scenarios both ways so the difference is visible, not asserted. That is the same bar I hold any agent to before it ships.
Where it runs
AI agent use cases: what businesses are automating
These are real automation categories, not theoretical examples. Each maps directly to a workflow where AI agent development services produce a measurable reduction in manual effort.
| Industry | AI agent use cases | Measurable outcome |
|---|---|---|
| Ecommerce | Product catalogue update agents, customer support triage agents, returns processing automation | Reduce support ticket handle time by 50 to 70% |
| Healthcare | Clinical document summarisation agents, prior authorisation agents, and patient query routing | Cut administrative processing time by 40 to 60% |
| Finance | Financial research agents, compliance monitoring agents, and report generation pipelines | Reduce analyst research time by 60 to 80% |
| Recruiting | Resume screening agents, candidate outreach agents, and interview scheduling automation | Reduce time-to-shortlist by 70% |
| Legal | Contract review agents, due diligence research agents, and clause extraction pipelines | Reduce document review time by 50 to 65% |
| SaaS products | In-app AI assistants, code review agents, and user onboarding automation agents | Reduce support load, improve activation rates |
Pricing
What it costs to hire an AI agent developer
Monthly retainer contracts are available for ongoing development and monitoring. Agency rates typically run 2 to 3 times higher for equivalent scope.
Single-task agent
A focused agent with clean API access and well-defined inputs and outputs. Built, tested, and deployed in 2 to 4 weeks.
Multi-agent system
Custom integrations, RAG pipeline setup, and full evaluation, in a scoped 6 to 10 week engagement.
Monthly retainer
Ongoing development and continuous monitoring. Standard for agents already in production.
Hourly consulting
Architecture reviews and feasibility checks for teams scoping their first agent.
How it gets built
How I build scalable AI agent solutions
A structured approach to design, develop, and deploy AI agents based on project requirements, focused on creating scalable and practical AI solutions ready for real-world use.
PHASE 01the decisive oneUse case and feasibility analysis
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PHASE 02before codeAgent architecture design
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PHASE 03after architectureFramework and model selection
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PHASE 04the buildAgent development and tool integration
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PHASE 05before launchEvaluation and red-teaming
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PHASE 06handoffDeployment and monitoring setup
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Tooling
The commercial AI agent tooling landscape
Beyond the LangChain and CrewAI build stack above, this is the broader landscape of agent platforms and providers worth knowing about, whether you build custom or evaluate an existing product first.
Reasoning cores
Advanced AI agent technology
Modern AI agent models enable intelligent automation by understanding instructions, planning actions, and executing tasks in real time.
Reasoning engine
Task planning, autonomous decision-making, tool usage, and structured output.
Voice agent
Real-time transcription, multilingual translation, voice-driven commands.
Enterprise agent
Long-context reasoning and compliance-ready automation for complex environments.
Visual agent
Image generation and creative asset production from prompt-based reasoning.
Creative agent
Expressive visuals and branding assets aligned with business intent.
Features
Why work with Shreyans Padmani
Building AI agents that automate tasks, handle workflows, and support day-to-day business operations.
Custom AI agents
Every business workflow is different, so I design AI agents based on your specific tasks, whether that is handling customer queries, processing data, or managing routine operations.
Task automation
I develop AI agents that can perform repetitive tasks automatically, helping save time and allowing teams to focus on more important work.
Seamless integration
AI agents are built to connect smoothly with your existing tools, APIs, or internal systems, making them easy to use within your current workflow.
Delivered work
Deep expertise in advanced AI technologies
A sample of shipped projects across recruitment, media, and customer experience.
AI resume screening for recruiters
Automate resume screening with AI-powered parsing and candidate matching. Reduce hiring time by 70% and shortlist top talent faster.
Read case study → Case studyAI video summarizer for businesses
Analyzing meetings and training videos manually consumed time and resources. AI enabled automatic speech-to-text and quick, concise video summaries.
Read case study → Case studyAI customer feedback classification with NLP
Open-text survey analysis was slow and inaccurate. NLP automated feedback classification for faster insights.
Read case study →About
Freelance AI agent developer
I am an independent AI developer with 5+ years building production AI systems, including autonomous agents, multi-agent pipelines, and LLM-powered workflow automation, for companies in SaaS, healthcare, fintech, and ecommerce. Every engagement is scoped, built, and shipped by the same person: me.
No account managers, no developer rotation, no team-level claims standing in for individual accountability. When you hire me, the person who scopes your agent's architecture is the same person who builds it, red-teams it, and deploys it to production.
FAQ
