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AI & Automation

AI Agent Developer

Build tool-using, memory-enabled agents with clear plans, permissions and tests.

LLM fundamentalsPrompt engineeringTool callingFunction callingMemory
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Build AI Agents That Work

A useful agent needs more than a clever prompt. It needs a well-defined goal, a limited set of tools, reliable input handling and a way to check its work. This track takes you from model responses to controlled multi-step systems.

Learn to separate planning from execution, store relevant context and return an honest result when a tool fails. Projects emphasise predictable behaviour and human approval for consequential actions. You will test the complete task, not only whether a response sounds convincing.

What you’ll learn

  • Choose between deterministic workflows and agent behaviour.
  • Implement tool boundaries and meaningful memory.
  • Evaluate an agent against representative tasks before deployment.

Curriculum, with a purpose.

01

AI agent fundamentals

Compare a chatbot, a fixed workflow and a tool-using agent. Choose the simplest architecture that can complete a clearly defined task and identify its boundaries.

02

Tool calling

Define structured inputs and outputs for tools. Validate arguments before execution, restrict permissions and keep a record of each action and result.

03

Multi-step requirements

Break a goal into dependencies, track progress and recover from partial failure. Add a maximum step count so an agent cannot continue indefinitely.

04

Memory

Separate temporary conversation context from durable records. Decide what should be saved, how it is retrieved and when it should be removed.

05

Content handling

Handle documents and user instructions separately. Practise source attribution, unavailable-information responses and resistance to instructions embedded in untrusted content.

06

API integrations

Connect a read-only external service first. Add authentication, response validation, timeouts and clear fallbacks before allowing write actions.

07

Decision making

Use explicit routing rules where possible and models where interpretation adds value. Add approval points for messages, changes to records and other external effects.

08

Agent testing

Build a small evaluation set covering correct answers, failed tools, ambiguous requests and unsafe inputs. Compare changes against repeatable expected outcomes.

Your practical project

A course support agent

Create an agent that answers from a small approved course catalogue, chooses a search or comparison tool and escalates questions about unconfirmed fees or schedules.

What you’ll produce

Tool definitions, a permission map, labelled test conversations and an evaluation report showing both passes and failures.

Skills and tools

LLM fundamentals · Prompt engineering · Tool calling · Function calling · Memory · API integration · Agent orchestration · Testing.

Example learning tools: LLM API sandbox, TypeScript or Python, JSON schemas, Evaluation datasets. The final toolset, access and any usage charges are confirmed for your batch. Platform names describe learning tools and do not imply a partnership or certification.

Who should join?

Learners with workflow foundations, developers and technically curious professionals who want to build controlled AI systems.

Your starting point

Comfort with structured data and API basics is helpful. Programming and workflow preparation can be discussed during counselling.

Before you enrol

Confirm the course duration, fees, schedule, learning format, software access and project review process in writing. The supplied training address is in Pandeypur, Varanasi. Ghazipur and Saidpur learners can discuss the available arrangements with the team.

Request a demo or ask for the course details.

See the thinking in motion

One enquiry.
A connected workflow.

Explore how the pieces work together. Select a path and step through a practical example.

Interactive learning simulation. No messages are sent and no external systems are connected.

AI Agent / learning demo0 of 7
  1. 01User goal
  2. 02Understand
  3. 03Plan
  4. 04Choose tool
  5. 05Execute
  6. 06Validate
  7. 07Respond

Start the simulation to follow the data, or use Next step to explore at your own pace.

Questions, answered

A few things you might be wondering.

Need help with your specific situation? Let’s talk it through.

Ask the Digitac team
Will my agent make decisions independently?

It can make bounded choices inside a workflow. External effects should have suitable permissions and approval steps. The project should define exactly which decisions stay with a person.

Does an agent need persistent memory?

Not always. Store only context that improves the task and can be handled responsibly. Start without long-term memory and add it for a specific reason.

How do I confirm fees and batch timing?

Request course details or call the Digitac team. Fees, duration, tool costs and availability must be confirmed before payment; they are not invented on this website.

How do I book free career counselling?

Use the enquiry form to prepare a WhatsApp message, call either number, or email learn@digitacsolution.in. The team must confirm a time with you; submitting an enquiry does not reserve a session.

Course enquiry

Let’s talk about AI Agent Developer.

Share your current skills and your next goal. Ask for the batch details and a learning plan that fits.

Let’s plan your next step.

Tell us what you want to learn or build. Prepare your enquiry, then send it directly on WhatsApp.

No payment is collected here. A session or demo is booked only after the team confirms it. You can also call 9236267769.

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