AI Agent Development Services

Find the most repetitive, annoying task your users hate doing. Don’t start with a massive, complex workflow. Pick a narrow use case where an agent can confidently save a user 10 minutes right now. Prove the value on a small scale before you try to automate the entire company.

Zero, until it earns it. If the agent is drafting an internal summary, let it run. But if it’s updating a production database, sending an email to a client, or touching money, put a human in the loop. The user must click “Approve” until the agent’s accuracy is bulletproof.

Agents are only as smart as the APIs they can access. You need clean data, strict user permissions, and clear business rules. If your internal database is a disorganized mess, the agent will just surface that mess faster.

Hardcoded guardrails. You don’t just ask the LLM nicely to behave in the prompt; you restrict its actual API access. We use strict permission scopes, audit logs, and mandatory human validation for anything destructive. If the agent goes off-script, the system blocks the API call.

You put them behind a paywall. Basic chat interfaces can be free, but actual agentic workflow execution, where the AI actually updates the CRM or writes the code, is a premium tier. You aren’t selling AI; you’re selling the manual labor the AI replaces.

Finding the best use case for AI Agent adoption can be difficult if you don’t have any experience in this technology and don’t understand its limitations and capabilities. Get consultation and strategy from the AI Agent experts who have successfully launched AI Agent products. The Consulting stage can be held as a brief discovery and preparation stage, or an extended Technical Strategizing.

AI Agent development may consist of two stages that are priced differently: AI Agent Consulting starts at $100 per hour, and Engineering services at $55 respectively. The final cost depends on complexity, integrations, and scalability requirements. Contact us to receive a precise quote.

A proof of concept can often be built in 3-6 weeks. Production-ready AI Agents, depending on complexity and integrations, typically require 2,5-4,5 months. Timelines vary with scope, compliance, and scalability goals. Contact us to get a precise estimation.

Absolutely. You don’t need to rebuild your whole platform. MobiDev engineers build the agent as a separate layer that hooks into your existing APIs and databases. It sits on top of your current architecture without breaking your legacy code.

We’ve worked on solutions for retail, travel and hospitality, wellness, fitness, healthcare, and SaaS products — industries with complex workflows and strong automation ROI.

Almost never on day one. Prompt engineering combined with good tool access and RAG will solve 95% of your problems. Don’t pay for expensive fine-tuning unless the agent needs to speak in a highly specific, proprietary domain language that standard models fail to understand.

By doing the heavy lifting. If your app requires users to click through five screens and fill out three forms, they will eventually churn. If they can just tell the agent to run the weekly report for client X, and it happens instantly, they will never leave your platform for a competitor.

Stop looking at “number of messages sent.” Look at the task completion rate. How many workflows did the agent actually finish? How often did the user have to manually correct the agent’s work? If the agent saves the user 20 minutes a day and they keep coming back, it’s a success.

Yes. We have delivered multi-agent architectures where agents collaborate, expanding capabilities beyond single-agent setups.

We suggest starting with one if you have no experience. Building a multi-agent system on day one is a great way to burn your budget on a science project. Build a single, highly focused agent first. Once that agent hits its limits and you actually need specialized roles like one agent for data parsing and another for writing, then you upgrade to a multi-agent architecture.

A chatbot talks. An agent acts. A chatbot can summarize a refund policy for you. An agent can check your CRM, verify the purchase date, calculate the refund amount, and trigger the Stripe API to actually send the money back.

Map out the exact manual workflow you want to automate. Document the APIs the agent will need to hit, the permissions it requires, and exactly what a successful outcome looks like. If you don’t know the exact business problem you are trying to solve, don’t write a single line of code yet.