Agentic AI Engineering Bootcamp
capability building,
designed for your organisation.
A custom-built corporate programme for AI engineers, ML engineers, senior software engineers, backend developers, and solution architects with 3+ years of experience who will design, build, and ship production-grade agentic systems. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Agent = LLM + tools + loop. The minimal definition and its implications
- Agents vs. chains vs. pipelines vs. workflows when each is the right abstraction
- Autonomy levels: scripted, supervised, semi-autonomous, autonomous and the operational implications of each
- Anti-pattern: when adding agency makes the system worse, not better
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Enterprise learning solutions built for corporate teams.
Go beyond standard classroom delivery with enterprise-ready learning infrastructure, managed execution, capability insights, and production-like practice environments designed for corporate scale.
Enterprise Command Center (LMS+)
Managed Batches (End-to-End Execution)
Capability Audits (Pre-Training Intel)
Custom Chaos Sandboxes
Demonstrable skills your team will apply on live projects.
Design agent architectures that fit the use case
Single-agent, supervisor-worker, planner-executor, critic-loop, hierarchical applied based on task structure, not framework default.
Ship production-grade agents on OpenAI Agents SDK or LangGraph
Capstone-grade agentic systems with full observability, evaluation, guardrails, cost controls, and graceful degradation.
Operate multi-framework fluency
OpenAI Agents SDK, CrewAI, LangGraph, AutoGen selected with judgement, not by default.
Pass Agentic AI Engineer Certification
Two attempts; cohort first-attempt pass rate 88%.
Lead an agentic AI initiative end-to-end
Equipped to architect, build, and own production agentic systems for the organisation.
Cut prototype-to-production time by 60%+
The discipline gap between 'works in notebook' and 'survives in production' closed during the bootcamp.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Has built ChatGPT-style chat applications but never shipped a multi-step or multi-agent system
- ·Familiar with LangChain at a chain level but not with agent orchestration patterns
- ·No working knowledge of OpenAI Agents SDK, CrewAI, LangGraph, or AutoGen
- ·Cannot reliably implement tool use, function calling, structured output across agent flows
- ·No production discipline agents demoed in notebooks but not deployed, monitored, evaluated
- ·Limited mental model of when an agent is the right pattern vs. when a pipeline is
Where they'll arrive
- ✓Architectural fluency can design and defend single-agent, supervisor-worker, planner-executor, and critic-loop patterns based on use case
- ✓Framework portability fluent in OpenAI Agents SDK, LangGraph, CrewAI, AutoGen with informed views on framework selection
- ✓Tool-use mastery implements robust function-calling, MCP-server integration, and external API orchestration with retry, fallback, and validation
- ✓Production discipline agents shipped with traces, evals, guardrails, cost dashboards, and rollback paths
- ✓Multi-agent orchestration has built and demoed working multi-agent systems with appropriate role specialisation and communication patterns
- ✓MCP-native development fluent in the Model Context Protocol for portable tool/resource integration
Built for L&D outcomes, not seat counts.
Architecture-first engineering discipline
This agentic AI corporate training moves engineers from notebook demos to production-grade systems with full observability, guardrails, and cost controls built in from day one.
Multi-framework fluency, not framework lock-in
Engineers graduate fluent in OpenAI Agents SDK, LangGraph training course content, CrewAI, and AutoGen with the judgement to select the right tool for each use case.
Production labs on real agent systems
Every module includes a hands-on lab. Engineers build, instrument, red-team, and optimise real agent systems not toy examples across 13 structured sessions.
Cost, latency, and reliability engineering
Corporate AI agent training that addresses the production triangle routing, caching, retries, circuit breakers, and graceful degradation so agents survive beyond the demo environment.
MCP-native from the ground up
Engineers build and consume Model Context Protocol servers across frameworks a critical production skill for agentic AI systems in 2026 and beyond.
Capstone reviewed by industry architects
Each team ships a production-grade multi-agent system reviewed by NovelVista's AI Practice and an invited industry CTO a real deliverable, not a course certificate alone.
A four-milestone path from skill gap to client-ready.
Agent foundations & architecture patterns
Establish a working mental model of agentic systems what agents are, autonomy levels, single-agent vs. multi-agent trade-offs, and when a pipeline is a better choice than an agent.
Framework immersion OpenAI SDK, LangGraph, CrewAI, AutoGen
Engineers build hands-on with all four major agent frameworks. Each framework lab covers architecture, tool use, orchestration patterns, and real failure modes encountered in production.
Production engineering MCP, memory, observability, safety
Teams integrate MCP servers, implement multi-tier memory, instrument full observability stacks, enforce guardrails, and red-team their own agents for adversarial robustness.
Capstone multi-week production build and panel review
Each learner team designs, builds, and deploys a production-grade multi-agent system with full observability, evaluation harness, cost dashboard, and red-team report reviewed by an industry CTO panel.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Trusted by Industry Leaders for Enterprise AI Upskilling
See why CEOs, CTOs, and business leaders collaborate with NovelVista
to discuss the future of AI, digital transformation, and workforce readiness.
- Exclusive AI leadership summits featuring enterprise decision-makers and technology experts
- Recognized corporate training partner for AI, Agile, DevOps, ITSM, and cybersecurity programs
- Trusted by organizations to build future-ready teams with practical, industry-focused learning
- Real conversations, real business challenges, and actionable AI transformation insights from industry leaders
Learn from domain experts with 15+ years of experience.
"AI transformation is not just about adopting new tools it’s about helping organizations build intelligent systems, scalable workflows, and future-ready teams that can innovate with confidence."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·AI engineers and ML engineers who want to move from model experimentation to production agentic systems as part of an agentic AI course corporate training programme
- ·Senior software engineers and backend developers with 3+ years of experience ready to architect and ship multi-agent solutions
- ·Solution architects responsible for designing agentic AI platforms, tool-use pipelines, and enterprise automation systems
- ·Engineering leads and technical managers looking to upskill their teams through structured corporate AI agent training
- ·Organisations in India and globally that need production-ready agentic AI training India cohorts delivered on their tech stack and project archetypes
Pre-requisites
- ·Proficiency in Python the bootcamp labs are code-intensive and require comfort with functions, classes, async patterns, and package management
- ·Hands-on experience calling REST APIs and working with JSON tool use and MCP integration are core lab activities
- ·Familiarity with LLM API usage such as OpenAI or Anthropic learners should have built at least one LLM-powered application before attending
- ·Basic understanding of software engineering concepts version control, environment management, and debugging are assumed throughout
Trusted by L&D leaders across the world.
Questions L&D teams ask before signing.
Agentic AI refers to AI systems that can reason, plan, and take autonomous actions to complete multi-step goals without constant human instruction. Unlike basic chatbots, Agentic AI agents can execute workflows, use tools, access enterprise data, and loop until a task is done. In 2026, 40% of enterprise applications will include AI agents (Gartner). Corporate teams that understand how to build, supervise, and govern these agents will drive the next wave of business automation.