The most in-demand emerging skill set in the market. Design, build, evaluate and ship autonomous AI agents — LLM fundamentals, prompt and context engineering, retrieval-augmented generation, tool calling, multi-agent orchestration, evaluation harnesses and production guardrails — through hands-on learning on real client briefs, followed by a 3-month industry internship.
Chatbots answer questions. Agents get things done — they plan, call tools, read and write to real systems, recover from failure, and run for minutes or hours rather than a single turn. Building them reliably is a genuinely different discipline from prompting a model, and it is where a large share of new AI hiring is concentrated.
The Agentic AI Program is a 12-Month Career Program built around that discipline. You start with the fundamentals — Python, APIs, and how large language models actually behave — then move into prompt and context engineering, structured output, function and tool calling, and retrieval-augmented generation done properly rather than as a demo.
From there the program goes where most courses stop: agent architecture and reliability. Planning and reasoning loops, memory design, multi-agent orchestration, human-in-the-loop checkpoints, cost and latency budgets, failure recovery, sandboxing and permissioning. Alongside it runs a full evaluation track — because an agent you cannot measure is an agent you cannot ship.
Because this field moves quickly, the curriculum is taught tool-agnostically: you learn the patterns first, then implement them across current frameworks. The program closes with a 3-month industry internship deploying agents on real client work under industry mentorship, wrapped in placement preparation and 100% job assistance.
One continuous career-focused track. You don't finish a course and then go hunting for experience — the industry internship is built into the program.
Live instructor-led sessions covering Python and API fundamentals, LLM behaviour, prompt and context engineering, RAG, tool calling, agent architecture, multi-agent orchestration, evaluation and production guardrails — each module shipping a working agent.
You join a delivery team building and deploying agents on live client work — scoping, implementing, evaluating and hardening them — with weekly mentor code reviews and stakeholder demos.
Three connected tracks: the LLM engineering foundation, agent architecture and orchestration, and the evaluation and safety layer that makes an agent shippable.
Practical learning throughout: each module is anchored to a real business scenario and ends in a deliverable. Every tool is taught the way it's used on the job — not in isolation.
The programming base an agent developer needs — including the async and error-handling patterns agents live or die on.
Before you build on a model, understand what it is and where it fails.
The highest-leverage skill in the whole field — treated as engineering, not folklore.
Giving a model access to knowledge it was never trained on, without lying about it.
The moment a model stops talking and starts doing.
The core architectural patterns behind every production agent.
When one agent isn't enough — and when adding a second one makes it worse.
You cannot ship what you cannot measure. This module is why our graduates get hired.
Everything that stands between a useful agent and an incident report.
Running agents where real users and real money are involved.
Choosing the right model — and knowing when adaptation beats retrieval.
Design, build, evaluate and deploy a production agent on a real client brief under industry mentor supervision.
Concrete, demonstrable capabilities built through practical learning — the kind you can show in a portfolio and defend in an interview.
Choose the right loop, memory model and tool surface for a task — and justify why a simpler design wasn't enough.
Design tool schemas models actually use correctly, handle side effects safely, and recover from partial failure.
Build evaluation harnesses, score trajectories, and run regression suites on systems that never give the same answer twice.
Design RAG pipelines with sound chunking, reranking and citation grounding — and measure whether they actually reduced hallucination.
Defend against prompt injection and tool abuse, apply least privilege, and design human-in-the-loop checkpoints where the stakes require them.
Deploy with tracing, cost monitoring and staged rollout, and debug a failed run from its trajectory.
The final 3 months are an industry internship building and deploying agents on live client work — in a field where almost nobody has real production experience yet.
You're assigned live agentic AI briefs from Ergebins Technologies' delivery pipeline and partner organisations — genuine systems, genuine constraints, genuine risk.
A practising AI engineer reviews your agent architecture, tool design, evaluation harness and guardrails weekly.
On completion you receive an Internship Certificate and a Project Experience Letter — verifiable experience for your résumé and background checks.
Portfolio development is built into the program. You leave with work you can walk a hiring manager through, line by line.
Build a grounded question-answering agent over a large internal document corpus, with chunking strategy, reranking, citation enforcement and a measured hallucination rate.
Ship an agent that reads and writes to real systems — orders, tickets, CRM — with idempotent side effects and human approval on destructive actions.
Orchestrate a planner and specialised sub-agents to research a topic, cross-check sources and produce a cited report, with cost and latency budgets enforced.
Build an agent that queries a warehouse, writes and validates SQL, produces charts, and refuses to answer when the data cannot support the question.
Deliver a deployed agent with a full evaluation harness, regression suite, tracing, injection defence and an honest results report on where it still fails.
The most technically demanding of our student programs. Some prior programming exposure makes the first two months considerably easier.
Certificates are awarded on assessed performance — completed modules, reviewed projects and a finished industry internship.
Awarded on assessed completion of all twelve modules, graded agent builds and your capstone project — issued by DICA, the education brand of Ergebins Technologies Private Limited.
Issued on completion of the 3-month industry internship, confirming the client engagement, your role and the duration of the placement.
A verifiable letter detailing the agentic AI systems you built and deployed, the stack you used and the outcomes achieved.
Common roles our graduates step into. Final compensation depends on background, performance and location.
Design and ship LLM-powered features and agents inside product teams, owning quality, cost and reliability.
₹10 – 24 LPABuild autonomous agents with tool access, memory and orchestration for enterprise workflows.
₹10 – 26 LPAOwn the prompt, retrieval and evaluation layer of production language applications.
₹9 – 22 LPAScope agentic AI opportunities for clients, design the architecture and lead delivery.
₹10 – 24 LPAOwn systematic prompt design and the evaluation harnesses that keep model behaviour measurable.
₹8 – 18 LPASit between product and model — turning ambiguous user problems into reliable agent behaviour.
₹9 – 20 LPAPlacement preparation starts in month seven, runs through your internship, and continues until you accept an offer.
One-on-one resume rewrites tuned to the roles you're targeting, plus a reviewed portfolio of your live projects, dashboards and capstone work.
HR, aptitude and technical mock interviews with recorded feedback. You rehearse the real rounds before you sit in them.
Business communication, stakeholder storytelling, group discussions and presentation practice — taught to corporate training standards.
Profile shortlisting, referrals into our hiring partner network, interview scheduling and continued career support until you accept an offer.
Placement preparation, mock interviews, and referrals into a recruiter network that hires for analytics, data science, and AI roles.
Company names and marks belong to their respective owners.
Outcomes from recent cohorts — live projects, internships and offers.
The evaluation module is the whole thing. Anyone can wire up an agent that demos well; almost nobody can prove theirs works. That's what I got asked about in every round.
I was a backend developer with three years of experience and no AI background. The internship put me on a live client agent with real tool access — I moved into an agentic AI role straight after.
Learning the patterns before the frameworks was the right call. Two of the libraries we started with changed completely during the program and it barely mattered.
The program is 9 months of learning + 3 months of industry internship = 12 months in total.
It's new, which is precisely the opportunity — demand for people who can build and evaluate agents currently exceeds supply by a wide margin. We manage the risk by teaching architectural patterns and evaluation discipline first and frameworks second, so your skills survive the next library churn.
No. This is an engineering program, not a model-training program. You'll work with existing foundation models rather than training them from scratch. Module two covers everything you need to know about how LLMs behave.
AI & Machine Learning is about building models — training, deep learning, computer vision, MLOps. Agentic AI is about building systems on top of existing models — tool use, orchestration, memory, evaluation and safety. Different roles, different day-to-day work. A counsellor can help you pick.
Yes. The 3-month industry internship is included in the 12-Month Career Program at no additional cost. You work on real client projects and receive an Internship Certificate and Project Experience Letter on completion.
The curriculum is taught tool-agnostically — patterns first, implementations second — and is refreshed each cohort to reflect the current landscape. You'll work hands-on across leading LLM providers, vector databases, agent frameworks and the Model Context Protocol.
Resume building, LinkedIn and portfolio development, GitHub review, mock interviews across HR, aptitude and technical rounds, soft skills training, hiring drive access, and referrals into our hiring partner network — including Technify, Infosys, LTIMindtree, EY, Capgemini, Grail Consultant, Intellicia and Thread Security.
Yes. We accept UPI, credit and debit cards, Visa and Mastercard, and offer No Cost EMI on selected cards and partner lenders, subject to eligibility and approval.
Share your details and a career advisor will call you back within one working day with the full curriculum, batch dates, fee structure and No Cost EMI options.
Book a free 15-minute career counseling session and reserve your seat before applications close.