About Course
About AI Agent Development Training
Delhi's AI demand is unlike the product cities because the buyer is often the state or an organisation serving it: ministries, departments, public-sector undertakings, consultancies preparing submissions and audit firms reviewing what was built. Work here has to survive scrutiny, documentation and procurement rules, so the value sits as much in traceability and reporting as in model accuracy.
The city also holds a large base of media, research, legal and policy organisations using models on documents — drafting, summarisation, discovery and records work — where accuracy and citation matter more than fluency. That produces steady demand for practitioners who can set evaluation standards and explain failures in writing. Graduates often move into programme, consulting or assurance roles rather than purely technical ones, because the ability to translate between technical and administrative language is the scarce part.
This Delhi page carries the same 12-week, 72-hour AI Agent Development Training syllabus, faculty and assessment as the master programme, delivered live online and in hybrid format, with classroom batches at our Thoraipakkam centre.
Course Overview
12-week AI Agent Development training (72 hours) covering reasoning loops, tool calling, agent memory, multi-agent orchestration, the Model Context Protocol, evaluation and observability. Classroom, Live Online and Hybrid.
Why Learn AI Agent Development Training
Government-facing programmes demand documentation that an auditor will accept, which rewards practitioners who can evidence every step rather than only demonstrate output.
Consulting and audit firms in the city review supplier-built systems, so people who can evaluate someone else's pipeline are hired alongside those who build one.
Media, legal and research organisations apply models to documents where a wrong answer is costly, making evaluation and citation discipline the deciding skill.
AI and data hiring sectors in Delhi
- Government, public-sector and policy bodies
- Consulting, audit and professional services
- Media, research and legal organisations
- Large enterprise headquarters and PSUs
Demand drivers behind Delhi AI and data hiring
- Procurement rules requiring documented, reviewable and auditable AI systems
- Public-sector digitisation programmes moving from records to decision support
- Client assurance work reviewing what a supplier actually built
- Records, discovery and drafting workflows being automated in regulated functions
Who Should Join This Course
- LLM Application Developers — Moving from single-shot RAG calls into multi-step work
- Backend Developers — Giving a model safe access to real tools and APIs
- Automation Architects — Replacing brittle RPA steps with model-driven decisions
- Platform Engineers — Building the agent runtime other teams will use
- Fresh Graduates — Targeting the agentic AI engineering track
- Technical Leads — Judging where an agent is appropriate and where it is not
Prerequisites
Working Python is required, including async patterns and exception handling, plus comfort with JSON and HTTP APIs. Prior exposure to calling a language model helps but is not required. Anyone who has automated a process in code will recognise the control-flow material immediately.
Learning Objectives
- Decide when an agent is justified and when a workflow would do the job
- Implement a reasoning loop with explicit stop conditions and step limits
- Expose tools to a model with validated schemas and safe error handling
- Give an agent durable memory without letting it accumulate bad context
- Orchestrate multiple agents with clear ownership of each step
- Use the Model Context Protocol to connect tools and data sources
- Evaluate an agent on task success rather than on answer quality alone
- Add observability, guardrails and a human approval gate to a live agent
What the Syllabus Covers
Each module is assessed against published Agentic AI Developer outcomes rather than on memorisation, covering Reasoning Loops, Tool Calling, Agent Memory, MCP, Multi-Agent Orchestration, Agent Evaluation together with tool design and idempotent actions, scoped memory with redaction, multi-agent orchestration over the Model Context Protocol, and an evaluation harness scored on task success rather than on answer quality.
Career Opportunities After This Programme
- AI Agent Developer
- Agentic AI Engineer
- Generative AI Engineer
- Automation Architect (AI)
- AI Solutions Engineer
- LLM Engineer
Hands-on Practice and Capstone
Capstone: Choose a real task and defend the agent decision, design tools with validated schemas and a dangerous-action confirmation gate, build a reasoning loop with step, token and time budgets plus loop detection, add scoped memory with summarisation and redaction, expose the internal API through a Model Context Protocol server, then ship with a trace dashboard, task-success eval suite, approval gate and runbook.
Production scenario: An agent booked the same refund twice because a tool call timed out and it retried without checking. This course treats that as the design lesson: tools are written to be idempotent, an irreversible action requires a human confirmation, the loop has a step budget it cannot exceed, and the trace shows exactly which decision produced the duplicate so the fix is a change to control flow rather than a lower temperature.
Course Duration, Mode and Certification
The course runs for 12 weeks (72 hours), three sessions a week of two hours each. Modes: Classroom / Live Online / Hybrid. Certificate issued: SkilBrill Course Completion Certificate. Assessment is against published Agentic AI Developer outcomes, and you finish with evidence you can show rather than a pass mark.
Is classroom training available in Delhi? Classroom batches run at our Thoraipakkam centre in Chennai. Learners in Delhi join the same live online or hybrid cohort taught by the same faculty, with the same assessment and placement support.
Why Train at SkilBrill
SkilBrill Training Institute in Thoraipakkam focuses on making you employable. Trainers are working AI, data and platform engineers, every module produces portfolio evidence rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹55,000 – ₹85,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to AI, data and platform engineering roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering evaluation, pipeline design and system-review scenarios
- Capstone walkthrough and portfolio review preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹8 – 25 LPA
Frequently Asked Questions
What does AI Agent Development training at SkilBrill cover?
Twelve weeks and 72 hours across eight modules: what makes something an agent, tool design and function calling, planning and termination, memory, multi-agent orchestration, the Model Context Protocol, reliability and evaluation, and a capstone agent shipped with tracing and an approval gate.
Do I need the LLM Application Development course first?
It helps but is not required. This course assumes you are comfortable calling a model and reading JSON, and it teaches retrieval where it is needed. If you have never built an LLM feature at all, taking the Generative AI course first is the calmer route.
What programming experience do I need?
Working Python: functions, classes, dictionaries, JSON, async patterns and exception handling. You will write tool implementations and a reasoning loop yourself, so comfort reading unfamiliar code matters more than any specific framework experience.
Why does the course teach the Model Context Protocol?
Because tool integration is where agents get expensive to maintain. MCP gives a standard way to expose data and actions, so one server serves several agents instead of a custom integration per assistant. You build a server over a real internal API in the course.
How is an agent different from an automation?
Automation follows a fixed path. An agent chooses its own next step from the model's reasoning, which is what makes it useful for ambiguous work and what makes it risky. One full module is about recognising when a deterministic workflow would simply be better.
Is certification included?
The syllabus follows agentic AI developer objectives so it works as preparation for emerging credentials in this area. Vendor exams are booked through their own programmes, and the SkilBrill completion certificate is issued by us on the capstone.
What do I leave with?
A working agent with validated tools, bounded loops, scoped memory, an MCP server over a real API, a trace dashboard, a task-success eval suite and a human approval gate for irreversible actions.
Is classroom training available in Delhi?
No. SkilBrill's classroom runs only in Chennai. Learners in Delhi join the same live online / hybrid cohort taught by the Chennai faculty, with proctored labs and the same placement support.
Which companies hire for these skills in Delhi?
Learners in Delhi typically target a mix of MNCs and startups such as HCL, TCS, IBM. The programme's placement support applies equally to Delhi learners.