Agentic AI in one paragraph
Most AI that businesses used until recently was reactive. You typed a question, the model replied, and the conversation ended there. Agentic AI adds three things on top of a language model: a goal ("get this lead to a booked site visit"), tools it is allowed to use (a calendar, a CRM, a WhatsApp number, a spreadsheet) and a loop. In the loop the model looks at the situation, picks an action, runs it, reads what happened and decides what to do next. It keeps going until the goal is met, a limit is hit or it hands over to a person.
Anthropic, which makes the Claude models, draws a useful line between two kinds of system. Workflows are "systems where LLMs and tools are orchestrated through predefined code paths". Agents are systems where "LLMs dynamically direct their own processes and tool usage". Agentic AI is the second kind, or a mix of both. The same article warns that agentic systems trade extra cost and latency for better results, so the simplest design that works is usually the right one.
How is agentic AI different from a chatbot or normal automation?
| Rule-based automation | Chatbot | Agentic AI | |
|---|---|---|---|
| What it does | Runs fixed steps: if X, then Y | Answers questions in conversation | Plans and takes actions towards a goal |
| Handles surprises | No, it breaks or skips | Can talk about them, cannot act | Yes, within the tools and limits you give it |
| Uses your systems | Yes, along a fixed path | Rarely | Yes, chooses which tool and when |
| Example | Zapier sends a Slack alert for every new form fill | Website bot answers "what are your timings?" | Agent reads a lead, checks stock, quotes a price on WhatsApp, books a call and logs it in Zoho |
| Cost per task | Lowest | Low | Higher: several model calls per task |
| Predictability | Highest | High | Lower; needs guardrails and review |
In practice the best systems blend the three. A fixed workflow handles the steps that never change (save the lead, send the receipt) and an agent handles the part that needs judgement (read the message, decide the reply, decide whether a person must step in). If you are comparing workflow tools for the fixed part, see n8n vs Make vs Zapier.
What does an AI agent need to act on its own?
- A model that can reason and call tools, such as GPT, Claude or Gemini.
- Tools: connections to the software it may touch, for example Google Calendar, Zoho CRM, a WhatsApp Business API number, Google Sheets or a payment-link API.
- Memory and context: the customer's history, your price list, your policies. Often supplied through retrieval from your documents (see what a RAG chatbot is).
- Instructions and limits: what it may and may not do, the maximum discount it can offer, when it must stop.
- A hand-over path to a named person, with the full context, when the agent is unsure or the customer asks for a human.
- Logs of every action so you can audit what happened and why.
Real examples of agentic AI for Indian SMEs
These are illustrations of what is practical today with off-the-shelf models and the tools most Indian businesses already use. They are not client case studies.
| Business | Goal given to the agent | Tools it uses | Where a person stays in charge |
|---|---|---|---|
| Real estate broker, Gurugram | Turn every portal lead into a booked site visit | Portal lead feed, WhatsApp, Google Calendar, CRM | Price negotiation and the visit itself |
| 3-doctor clinic, Pune | Fill tomorrow’s empty slots | Clinic software or Sheets, WhatsApp, AI calling | Anything clinical; the agent only books |
| D2C skincare brand | Confirm COD orders and cut returns | Shopify, WhatsApp, courier tracking | Refund approvals above a set amount |
| CA firm | Collect pending documents before the GST due date | Client list in Sheets, WhatsApp, Google Drive | Review of every filing |
| Distributor | Chase overdue invoices politely | Tally export, WhatsApp, UPI payment links | Credit decisions and disputes |
| Coaching institute | Qualify enquiries and book demo classes | Website forms, WhatsApp, AI calling, CRM | Fee discounts and admissions |
Notice the pattern. Each agent has one clear goal, a short list of tools and a defined point where it stops. The founder of Silvane built this kind of automation at Vetic, a pet-care chain, where daily doctor performance reports, incentive calculations and the mailers that go with them now run without anyone building them by hand. The lesson from that work is that the boring, repeated, rule-heavy task is where an agent pays for itself first.
What are the risks of agentic AI?
Because an agent acts, its mistakes cost more than a chatbot’s. The common failure modes are well known:
- Wrong action from a wrong reading. The agent misreads a message and sends the wrong quote, or books the wrong slot.
- Compounding errors. A small mistake early in a multi-step task carries into every later step.
- Runaway cost. An agent stuck in a loop can make hundreds of model calls. Set hard limits.
- Prompt injection. Text inside an email, PDF or web page tells the agent to do something you never intended, such as forward data.
- Data exposure. Customer data flows to model providers. Under the Digital Personal Data Protection Act, 2023 you remain responsible for how it is used.
- Vendor hype. Gartner has called out "agent washing", where chatbots and RPA tools are relabelled as agentic AI, and estimated only about 130 vendors in the space are genuine.
Gartner’s June 2025 prediction is worth reading before you buy: over 40% of agentic AI projects will be cancelled by the end of 2027 because of rising costs, unclear business value or weak risk controls. The same firm expects that by 2028, 15% of day-to-day work decisions will be made autonomously by AI agents, up from almost none in 2024. Both can be true. The projects that survive are narrow and measured.
Guardrails checklist before an agent goes live
- Write the goal as one sentence with a number attached, such as "book 30% of new leads within 24 hours".
- Give the agent the fewest tools it needs, with read-only access wherever possible.
- Require human approval for money movement, refunds, discounts and anything sent to more than a few people at once.
- Cap steps and spend per task, and alert a person when a cap is hit.
- Keep a full log of every message, tool call and decision.
- Treat text from emails, PDFs and websites as data, never as instructions.
- Tell customers they are talking to an AI assistant, and give them an easy way to reach a person.
- Use business AI plans that do not train on your data, and build in your own accounts.
- Run it alongside your team for two to four weeks before letting it act alone.
- Review a sample of conversations every week and fix the instructions.
A 30-day pilot plan for your first agent
| Week | What happens | What you check |
|---|---|---|
| 1 | Pick one task, write the goal and limits, connect read-only tools | Can the agent see the data it needs? |
| 2 | Agent drafts actions; a person approves every one | How many drafts were right without edits? |
| 3 | Agent acts alone on low-risk cases, drafts the rest | Errors, hand-overs, cost per task |
| 4 | Compare with the month before: time saved, response time, conversions | Keep, fix or stop |
If the agent is not clearly better than the old way after four weeks, stop or narrow it. A cancelled pilot that cost a few weeks is a good outcome compared with a large project that drifts for a year.
How much does agentic AI cost to run?
Cost has two parts. The build is a one-time job: mapping the process, connecting the tools, writing instructions and testing. The run cost is model usage plus the software it touches, such as WhatsApp conversation charges or call minutes, and someone to review and improve it. An agent that handles a few hundred WhatsApp leads a month usually costs far less in model usage than in the messaging platform itself, but a badly designed agent that loops can reverse that. For a fuller breakdown see AI agent development cost in India.
How Silvane can help
Silvane builds custom AI agents for Indian SMEs on the tools you already use, inside your own accounts. Start with a ₹199 discovery call and AI audit for founding members (normally ₹15,000, credited to the build). Starter builds begin at ₹50,000 with a run plan from ₹15,000 a month; tools and AI usage are billed at cost and GST is extra. If you want the fixed, predictable part first, our workflow automation service is often the better starting point.
Sources: Gartner press release, June 2025 · Anthropic: Building effective agents