Artificial Intelligence

Advanced Agentic AI for Enterprise Bootcamp

Online

On-site

Hybrid

Advanced Agentic AI for Enterprise Bootcamp

Design, build, and deploy enterprise-ready Agentic AI systems. This bootcamp focuses on multi-agent orchestration, autonomous workflows, enterprise RAG, observability, governance, and deployment. Teams learn how to move from experimental LLM apps to auditable, secure, and compliant Agentic AI solutions aligned with real business workflows.

Duration:

5 days

Level:

Advanced

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1500+ users onboarded

Who will Benefit from this Training?

AI and ML Engineers
Data Scientists and MLOps Engineers
Platform and DevOps Engineers
Product Managers working on AI products
Enterprise Architects and Tech Leads

Training Objectives

Understand enterprise multi-agent architecture patterns and governance-first design principles.
Build tool-using agents with safe input/output contracts, state handling, and retry-based execution patterns.
Implement enterprise-grade agents using LangChain, DSPy, LangGraph, and CrewAI for reliable orchestration and collaboration.
Design and build production-ready RAG pipelines with traceable citations, source metadata, and audit trails.
Apply RAG quality controls including permission-aware retrieval, document freshness strategies, and hallucination reduction guardrails.
Implement planning and reflection loops with safety guardrails including tool allowlists and prompt injection defenses.
Add observability and compliance-ready logging with tracing, PII-aware practices, dashboards, and alerting.
Build evaluation and testing frameworks for agent systems including regression testing and red-team style validations.
Deploy enterprise agent systems using secure access controls, tenancy boundaries, quotas, rate limiting, and secrets management.
Deliver a capstone production-style enterprise multi-agent solution with architecture documentation, governance controls, and an end-to-end working demo.

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Key training modules

Comprehensive, hands-on modules designed to take you from basics to advanced concepts

Module 1: Enterprise Multi-Agent Architecture (Governance-First Design)
1. What multi-agent systems solve in enterprises (automation, decision support, ops copilots)
2. Key roles in an agent system (planner, executor, verifier, reviewer)
3. Enterprise governance requirements (auditability, traceability, access control)
4. Design patterns (orchestrator-agent, swarm, supervisor-worker)
5. Hands-on: Activity: Design a multi-agent system blueprint for an enterprise workflow
Module 2: Tool-Using Agents Fundamentals (Function Calling + Tooling Patterns)
1. Agent tool use patterns (search, retrieval, actions, workflows)
2. Tool schemas and safe input/output contracts
3. State management basics (memory, context boundaries)
4. Error handling and retries for tool execution
5. Hands-on: Lab: Build a simple tool-using agent that calls 2 tools and returns structured output
Module 3: LangChain Agents (Enterprise-Ready Implementation)
1. LangChain fundamentals (chains, tools, agents)
2. Runnable patterns and structured outputs
3. Prompt templates and role-based constraints
4. Secure tool execution and sandboxing concepts
5. Hands-on: Lab: Implement a LangChain agent that performs multi-step task execution with tool calls
Module 4: DSPy for Reliable Agent Behavior (Prompt Optimization Patterns)
1. Why DSPy (reduce prompt brittleness, improve consistency)
2. Signatures, modules, and programmatic prompting
3. Optimization loop overview (teleprompters, metrics)
4. Evaluation-driven improvement for agent outputs
5. Hands-on: Lab: Build a DSPy program to improve answer quality and reduce hallucinations
Module 5: LangGraph for Stateful Multi-Step Agents (Orchestration)
1. Graph-based agents (nodes, edges, state, transitions)
2. Handling branching, retries, and recovery paths
3. Human-in-the-loop checkpoints (approval gates)
4. Building reliable workflows (idempotency and re-entrancy concept)
5. Hands-on: Lab: Create a LangGraph workflow with planner → executor → verifier loop
Module 6: CrewAI for Multi-Agent Collaboration (Role-Based Teams)
1. CrewAI concepts (agents, tasks, crew orchestration)
2. Defining roles and responsibilities for enterprise use
3. Task decomposition and parallel execution patterns
4. Managing shared context and preventing cross-agent contamination
5. Hands-on: Lab: Build a CrewAI team with 3 agents (researcher, builder, reviewer) for an enterprise task
Module 7: Enterprise RAG Design (Traceable Citations + Audit Trails)
1. RAG fundamentals (chunking, embeddings, retrieval, re-ranking)
2. Enterprise RAG requirements (permissioning, tenancy, governance)
3. Traceable citations and grounded responses
4. Audit trails (who asked what, what sources were used, when)
5. Hands-on: Lab: Build a RAG pipeline that returns answers with citations and source metadata
Module 8: RAG Quality Controls (Freshness, Access, and Hallucination Reduction)
1. Reducing hallucinations with strict grounding rules
2. Document freshness and versioning strategy
3. Permission-aware retrieval (RBAC/ABAC concepts)
4. Fallback strategies (no answer, escalate, human review)
5. Hands-on: Lab: Add policy checks and refusal logic when documents are missing or restricted
Module 9: Planning, Reflection, and Safety Guardrails
1. Planning strategies (task decomposition, step planning, goal validation)
2. Reflection loops (self-checking, verifier agent pattern)
3. Safety guardrails (tool allowlists, restricted actions, scope limits)
4. Prompt injection defenses for RAG and tool use
5. Hands-on: Lab: Implement planner + verifier loop with safety filters and prompt injection checks
Module 10: Observability and Compliance-Ready Logging
1. What to log for enterprise compliance (inputs, outputs, tool calls, sources)
2. Tracing agent steps and decisions (run-level telemetry)
3. PII redaction and data minimization concepts
4. Operational dashboards and alerting for agent failures
5. Hands-on: Lab: Add structured logs + traces for agent runs and generate an audit report
Module 11: Evaluation and Testing for Agent Systems
1. Offline vs online evaluations (test suites, golden datasets)
2. Metrics (groundedness, citation accuracy, task success rate)
3. Regression testing for prompts and tools
4. Red-team testing basics (jailbreaks, injection, data leakage)
5. Hands-on: Lab: Build an evaluation harness and run tests against agent changes
Module 12: Deployment Patterns for Enterprise Agents
1. Deployment models (internal API, chatbot UI, workflow integration)
2. Access controls and tenancy boundaries
3. Rate limiting, quotas, and cost controls
4. Secure secrets handling and key management
5. Hands-on: Lab: Package the agent system as a service with environment configs and safe runtime controls
Module 13: Capstone Project (Production-Style Enterprise Multi-Agent System)
1. Capstone goal: Deliver a governance-aligned multi-agent solution
2. Implement tool-using agent team (LangChain/LangGraph/CrewAI)
3. Add enterprise RAG with citations and audit logging
4. Implement planning + reflection + safety guardrails
5. Deliver observability, evaluation, and compliance-ready reporting
6. Hands-on: Capstone: Submit architecture diagram, governance controls, and end-to-end working demo

Hands-on Experience with Tools

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Training Delivery Format

Flexible, comprehensive training designed to fit your schedule and learning preferences

Opt-in Certifications

AWS, Scrum.org, DASA & more

100% Live

on-site/online training

Hands-on

Labs and capstone projects

Lifetime Access

to training material and sessions

How Does Personalised Training Work?

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Skill-Gap Assessment

Analysing skill gap and assessing business requirements to craft a unique program

1

Personalisation

Customising curriculum and projects to prepare your team for challenges within your industry

2

Implementation

Supplementing training with consulting support to ensure implementation in real projects

3

Why Agentic AI for your business?

Enterprise-ready autonomy: Move from static chatbots to multi-agent systems that reason, plan, and act.
Operational efficiency: Automate complex workflows across finance, compliance, and operations.
Governed AI adoption: Build auditable, traceable, and policy-aligned AI agents.
Future-proof architecture: Adopt emerging standards like MCP and ACP for cross-tool interoperability.

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Frequently Asked Questions

1. What are the pre-requisites for this training?

The training does not require you to have prior skills or experience. The curriculum covers basics and progresses towards advanced topics.

2. Will my team get any practical experience with this training?

With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.

3. What is your mode of delivery - online or on-site?

We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.

4. Will trainees get certified?

Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.

5. What do we do if we need further support after the training?

We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.

Advanced Agentic AI for Enterprise Bootcamp

Who will Benefit from this Training?

Training Objectives

Build a high-performing, job-ready tech team.

Key training modules

Hands-on Experience with Tools