AI Agent Development
Naman Gundaniya builds AI agents that take repetitive daily work off a team's plate — reading, deciding and acting across your tools using the Claude and OpenAI APIs. The difference between an agent and a demo is the engineering around it: tool design, guardrails, evaluation, and a clear line where a human takes over.
100% Job Success · Top Rated Plus on Upwork · replies in under 24h
Who this is for
- Teams spending hours a day on triage, data entry or follow-ups a model could handle
- Products adding an assistant that acts, not just answers
- Founders with an agent prototype that is not reliable enough to ship
- Operations leads who want fewer manual steps without losing oversight
What you get
- A production agent with tool calling across your APIs and data
- Guardrails: permissions, spend limits and hard stops on risky actions
- Human-in-the-loop approval where a wrong action would be expensive
- An evaluation suite that scores the agent on real scenarios before every change
- A full audit log of every decision, tool call and outcome
- Cost and latency instrumentation per run
- 30 days of post-launch support
Agents that act, not chatbots that answer
A chatbot produces text; an agent produces outcomes. It reads the state of your systems, decides what needs doing, calls the right tool, and checks the result — the same loop a person runs when they triage an inbox or reconcile two systems. That loop is only as good as its tools: each one is a narrow, typed, permissioned interface to your data, because an agent with vague tools makes vague decisions. The model is a component. The tool layer is the product.
The daily tasks worth handing to an agent
The best candidates combine repetition with judgement: triaging support tickets and inbound email, qualifying leads before a human sees them, extracting structured data from messy documents, keeping records consistent across tools, drafting the routine reply that a person only needs to approve. Individually these are ten-minute tasks; collectively they are someone's whole afternoon, every day. An agent absorbs the volume and escalates the exceptions — which is exactly the split a good hire would make.
Autonomous where safe, human-approved where not
Not every action deserves autonomy. Reading, classifying and drafting can run unattended; sending money, deleting records or emailing a customer should pause for a human click. That boundary is designed explicitly at the start — per tool, per action — rather than discovered after an incident. The practical pattern is an agent that does ninety percent of the work and presents the risky ten percent for approval, which keeps the speed without betting the business on a language model's judgement.
Guardrails and evals are the actual product
An agent that behaved well in a demo has proven nothing. Before anything touches production, it runs against an evaluation suite of real scenarios with expected outcomes — and that suite runs again on every prompt or tool change, so regressions surface before deployment instead of in front of a customer. At runtime, hard limits do what prompts cannot: permissioned tools, spend caps, iteration ceilings, and an audit log of every decision so behaviour is inspectable rather than mysterious.
When an agent is the wrong tool
If every step of a process can be written as a rule, you do not want an agent — you want automation. A cron job and a well-tested rule are cheaper, faster and perfectly predictable, and no model can beat that for deterministic work. Most real systems end up as a deterministic workflow with one or two agent steps inside it, not an agent running the show. Part of the discovery call is saying so when an agent is the expensive way to solve your problem.
How the engagement works
- 01
Discovery
Map the task as a human actually does it — inputs, decisions, tools touched, and the exceptions. Half the value is discovering which steps need no intelligence at all.
- 02
Design
Define the agent's tools, permissions and stopping conditions, plus the evaluation scenarios it must pass before it touches anything real.
- 03
Build
Implement the tools and the agent loop, run it against the evaluation suite, then shadow-run it alongside the human process until the two agree.
- 04
Ship
Deploy with audit logging, cost tracking and alerting, plus 24-hour response times for 30 days after launch.
Technology
- Models & orchestration
- The Claude and OpenAI APIs with native tool calling; LangChain and LangGraph where multi-step orchestration earns the dependency rather than by default.
- Tools & integration
- Typed, schema-validated tool interfaces over your REST APIs, databases and MCP servers — an agent is exactly as safe as its tools.
- Evaluation
- Scenario suites with expected outcomes, re-run on every prompt or tool change, so regressions surface before deployment rather than in production.
- Operations
- Audit logs, per-run cost tracking, rate limits and failure alerting — an agent without observability is a liability with API keys.
Proof
Task-Flow - AI Task Management
Task-Flow is a MERN-stack task management application with role-based access control and AI-assisted prioritisation. It covers team task tracking, assignment and collaboration, with the AI layer suggesting priority order and surfacing productivity patterns rather than replacing human judgement.
AI Voice Agent for Education
An AI voice agent for education built with Next.js and the Gemini API. Students speak instead of typing; speech is transcribed, structured into organised notes, and made queryable. Built around the observation that voice removes the friction of note-taking during a lecture.
Frequently asked
What can an AI agent actually automate?
Work that combines repetition with judgement: triaging inboxes and tickets, qualifying inbound leads, extracting data from unstructured documents, keeping records in sync across tools, and drafting routine responses for human approval. If a task follows fixed rules, plain automation is cheaper; if every item needs discretion, an agent can absorb most of the volume and escalate the exceptions.
How do you stop an agent going off the rails?
By constraining what it can do rather than hoping prompts hold. Tools are permissioned and schema-validated, destructive actions require human approval, spend and iteration limits are hard-coded, and an evaluation suite must pass before any change ships. Every decision is logged, so behaviour is auditable rather than mysterious.
Do I need an agent or an automation?
If every step of the process can be written as a rule, you want automation — cheaper, faster and fully predictable. An agent earns its place only where a step genuinely requires judgement on unstructured input. Most production systems are a deterministic workflow with one or two agent steps inside it, and the discovery call is where that split gets made honestly.
What does an AI agent cost to run?
Ongoing cost is model tokens plus infrastructure, and architecture dominates both: routing routine items to smaller models, caching stable context, and keeping deterministic steps out of the model routinely change the bill by an order of magnitude. Per-run cost tracking is part of every build, and the build itself is quoted after a free 30-minute scoping call.
How long does an agent take to build?
A well-scoped single agent — one process, a handful of tools — typically lands in two to five weeks including evaluation and shadow-running. What extends a timeline is rarely the agent loop; it is integration, because every system the agent touches needs a safe, tested tool interface. The scoping call produces a real estimate against your actual process.
Start with a 30-minute call
Scope the work, agree a timeline, and find out whether this is the right fit — no obligation either way.
Email Naman