®AI agents that actually take action — not just chat.
Custom autonomous agents built for your business workflows. They qualify leads, book meetings, file tickets, draft reports, and call APIs. We build them with the right model, the right tools, and the guardrails that keep them safe in production.
When to hire us
- You have a repetitive multi-step workflow worth automating
- You want a sales / support / ops agent that handles real tasks
- You've prototyped with ChatGPT but need production reliability
- You need an agent that calls your APIs, not just answers questions
What are AI agents and how do they work?
An AI agent is software that can take autonomous actions to complete a task — calling APIs, reading databases, sending messages, booking calendar slots, or filing tickets — without requiring human approval at each step. Unlike a chatbot that only answers questions conversationally, an agent follows a goal, selects the right tools to pursue it, and executes a sequence of steps until the task is done. The distinction matters: a chatbot tells a user how to file a support ticket; an AI agent files it for them. Nowgray builds custom AI agents for businesses that have identified specific workflows worth automating — lead qualification, meeting booking, invoice processing, report generation, compliance monitoring — and want them handled by software that acts, not just advises.
Our agent design process starts with workflow mapping, not model selection. Before choosing Claude or GPT, we understand the task boundary: what the agent should do, what it should never do, and where a human should stay in the loop. Most production failures in AI agents come from poorly defined boundaries — agents that act outside their intended scope, or that get stuck when reality doesn't match the expected flow. We address this through structured tool design, action limits, spending caps, and human-in-the-loop checkpoints at critical decisions. Guardrails aren't a feature we add at the end; they're part of the system architecture from the first design session.
We've shipped production AI agents on Claude, GPT-4o, and Gemini, and we don't favour one over another — the right model depends on the task's latency requirements, cost targets, and capability needs. Our architecture uses an abstraction layer so you can swap models as better or cheaper options become available. Every agent we build comes with a monitoring dashboard that logs every action taken, every tool call made, and every error caught — so you can see exactly what the agent is doing and measure its accuracy over time. This observability layer is what separates a production agent from a demo: you know when it's working, you know when it isn't, and you can fix it before users see the problem.
The capabilities, spelled out.
Custom agent design
We design the agent's role, tools, prompts, and decision boundaries around your specific workflow — not generic templates.
Tool use & API calling
Agents that read and write to your CRM, CMS, database, calendar, payment gateway — anything with an API.
Guardrails & approvals
Human-in-the-loop checkpoints, action confirmations, spending limits, content filters. Agents act safely, not autonomously dangerously.
Memory & context
Short-term + long-term memory so the agent remembers users, decisions, and prior conversations across sessions.
Multi-agent orchestration
When one agent isn't enough, we design agent teams — specialist agents that coordinate on complex tasks.
Evals & monitoring
Production observability — every action logged, accuracy measured, regressions caught before users see them.
Our default stack.
We'll pick the right tools for your project — but if you don't care, this is what we usually reach for.
Outcomes, not just hours.
- Agents that take real action — not just chat
- Costs predictable: per-action token + dollar metrics
- Quality measurable: evals catch regressions before deploy
- Vendor-neutral architecture — swap models as they get better
Common questions.
What is the difference between an AI agent and an AI chatbot?
A chatbot answers questions in a conversation. An AI agent takes actions — it can call APIs, update your CRM, book meetings, send emails, or process documents without a human approving each step. Chatbots are reactive; agents are proactive. If you want something to actually do the work rather than explain how to do it, you need an agent, not a chatbot.
How long does it take to build an AI agent?
A single-purpose agent (lead qualifier, meeting booker, support ticket filer) typically takes 3–6 weeks from the first scoping call to production deployment. Complex multi-agent systems with deep integrations take 8–16 weeks. We scope accurately before starting and deliver in two-week sprint cycles with live demos.
How much does building an AI agent cost?
A focused single-workflow agent starts from ₹2–5 lakh. Multi-agent systems and enterprise-grade agents with complex integrations range ₹8–25 lakh. Ongoing API costs (to Anthropic or OpenAI) are separate and typically ₹5,000–50,000/month depending on usage volume. We provide cost estimates for both build and ongoing runtime before you commit.
Can the agent connect to our existing CRM and software?
Yes — that's the norm, not the exception. Our agents connect to CRMs (Salesforce, Zoho, HubSpot), databases, Google Workspace, calendars, payment gateways, and any system with an API. For legacy software without an API, we can build a data bridge. The agent's tools are designed around what your business actually uses.
What prevents the agent from doing something it shouldn't?
We design explicit action boundaries, spending limits, and human-in-the-loop checkpoints for every high-stakes action. The agent is given a defined list of tools it's allowed to call and cannot act outside that list. For irreversible actions — sending emails, making payments, deleting records — we implement confirmation steps. Every action is logged so you can audit exactly what the agent did and when.
Let's scope your autonomous ai that gets work done.
Tell us what you have in mind — we'll come back with a clear plan, timeline, and quote.