AI Engineering · Practical career preparation

Agentic AI Engineer

Tools, State, Authorization & Durable Workflows

Build agents that act within clear permissions—and recover when execution fails. Develop a bounded AI agent with typed tools, explicit state and observable execution. Test authorization, retries, restarts and hostile inputs, then compare the agent with a deterministic workflow on the same task.

12 core modules Diagnostic-based preparation Three guided projects One independently assessed capstone
RoboEdify track record
10,000+
alumni transformed
1,000+
hiring partners
4.8/5
average class rating
87%
placed in 6 months
10+
years of training
Where our AI alumni work
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer

What does this engineering pathway involve?

An agentic AI system uses model outputs to propose or select steps in a workflow. Engineering it requires more than connecting tools: the application must control authority, state, side effects and recovery. This course makes those responsibilities explicit in an isolated maintenance/service workflow.

Learning approach

Build systems you can inspect, test and explain.

Each practical task includes an explicit requirement, a baseline and a failure investigation. You keep versions and results so another learner can reproduce your work. The final assessment includes an unfamiliar debugging exercise as well as your prepared project.

Learning format

Learn through classes, projects and individual feedback.

The program combines live mentor-led classes, guided labs, project work and support sessions. An advisor can explain the current on-campus, online or working-professional format and course-specific software/compute access before enrollment.

On-campusLive onlineWorking-professional format
Who should join

A common core, with preparation matched to your starting point.

This is an application-engineering pathway. Learners need Python, APIs, structured model outputs, testing and basic database readiness. Generative AI Engineer or equivalent experience is recommended. Graduates and working engineers complete the same practical assessment, with targeted preparation where needed.

A diagnostic identifies preparation needs. Graduates and working engineers complete the same practical exit requirements. Relevant prior learning can be recognized without repeating equivalent foundation work.

Learning outcomes

Leave with skills you can demonstrate.

Define bounded agent behaviorSpecify tools, trust boundaries and terminal outcomes.
Build typed tool interfacesValidate inputs and apply server-derived authorization.
Manage execution stateUse explicit transitions, budgets and cancellation.
Handle retries and restartsDistinguish replay, conflicting requests and uncertain effects.
Evaluate agent quality and riskTest normal, denied, ambiguous and hostile cases.
Defend when an agent is usefulCompare with a deterministic workflow and document operating limits.
Readiness areas

What you should be able to do before you start.

Application developmentPython, HTTP/JSON, validation, tests and error handling.
LLM integrationModel adapters, structured outputs and frozen evaluation cases.
State and databasesBasic SQL, transactions, identifiers and request lifecycle.

A diagnostic identifies preparation needs. Graduates and working engineers complete the same practical exit requirements. Relevant prior learning can be recognized without repeating equivalent foundation work.

Course curriculum

Twelve modules, from task definition to an independent capstone.

Every module includes topics, a practical task, a failure to investigate, deliverables and an assessed outcome.

01

Agent scope and deterministic baselines

Engineering core
+
Topics
•Workflow versus agent
•Model/planner/executor separation
•Success/failure states
•Trust boundaries
•Allowed actions
Hands-on lab
Define a small maintenance workflow and a deterministic reference implementation.
Failure scenario you solve
The model is treated as the authority to decide what a user is permitted to do.
You build
Scope, trust diagram and baseline workflow.
Assessed outcome: Explain where model flexibility is useful and where deterministic control is required.
02

Typed tools and execution contracts

Engineering core
+
Topics
•Schemas
•Semantic validation
•Allowed tools
•Bounds
•Validation order
•Effect receipts
•Unknown fields
Hands-on lab
Build tools that read a fixture and propose/update a bounded local record through validated inputs.
Failure scenario you solve
A valid-looking payload contains an out-of-range value that should never change state.
You build
Tool schemas, gateway and rejection tests.
Assessed outcome: Show that invalid requests produce no mutation.
03

Identity, authorization and approval

Engineering core
+
Topics
•Server-derived identity
•Scoped access
•Policy checks
•Approval binding
•Expiry/revalidation
•Audit context
Hands-on lab
Test authorized and denied operations and bind approval to the exact proposed action.
Failure scenario you solve
An approved request is changed before execution without a new check.
You build
Policy tests and approval/state trace.
Assessed outcome: Verify permissions at execution rather than trusting the planner or client claim.
04

Planning, memory and execution budgets

Engineering core
+
Topics
•Explicit states
•Checkpoints
•Context versus durable memory
•Step/cost limits
•Loops
•Terminal errors
•Cancellation
Hands-on lab
Implement a bounded planner loop and stop repeated proposals with a clear unresolved-state report.
Failure scenario you solve
The agent repeatedly retries the same impossible task.
You build
State machine, budget controls and cancellation tests.
Assessed outcome: Demonstrate finite execution and visible terminal outcomes.
05

MCP interfaces and scoped integration

Engineering core
+
Topics
•Host/client/server roles
•Tool discovery
•Schemas
•Transport boundaries
•Identity versus protocol
•Resource trust
Hands-on lab
Connect one local teaching MCP server and validate the same tool policy through the adapter.
Failure scenario you solve
A discovered tool is assumed safe or authorized merely because it is available.
You build
MCP adapter, interface contract and denied-call tests.
Assessed outcome: Explain what protocol discovery supplies and what authorization the application still owns.
06

Durable workflows and restart

Engineering core
+
Topics
•Checkpointing
•Resume semantics
•Deterministic/non-deterministic steps
•Task persistence
•Paused execution
Hands-on lab
Use a qualified workflow framework or explicit state store to resume a controlled task after restart.
Failure scenario you solve
A resumed workflow re-runs a step with an external effect.
You build
Checkpointed workflow and crash-point tests.
Assessed outcome: Identify which steps may replay and how their effects are protected.
07

Transactions, idempotency and uncertain effects

Engineering core
+
Topics
•Operation IDs
•Payload conflicts
•Database transaction scope
•Deduplication
•Receipts
•Remote-effect uncertainty
•Reconciliation
Hands-on lab
Replay local operations and simulate a remote timeout after a possible commit.
Failure scenario you solve
The application cannot tell whether a timed-out external request succeeded and blindly repeats it.
You build
Idempotency gateway and reconciliation cases.
Assessed outcome: Distinguish local transaction guarantees from remote exactly-once assumptions.
08

Agent evaluation and observability

Engineering core
+
Topics
•Frozen task sets
•Baseline comparison
•Traces
•Tool-call correctness
•Repeated runs
•Denied/ambiguous cases
•Redacted logging
Hands-on lab
Evaluate a deterministic workflow and model planner on the same bounded cases.
Failure scenario you solve
Task success hides unauthorized intermediate actions or excessive retries.
You build
Evaluation harness, traces and comparison report.
Assessed outcome: Assess both final outcomes and execution behavior.
09

Hostile observations and failure containment

Engineering core
+
Topics
•Prompt injection
•Untrusted tool/doc output
•Least privilege
•Sensitive data
•Misleading tool responses
•Containment
Hands-on lab
Inject instructions into a fixture document and test unknown-tool and role-spoofing attempts.
Failure scenario you solve
The planner treats retrieved text as permission to invoke another tool.
You build
Adversarial cases and containment report.
Assessed outcome: Preserve policy boundaries even when model proposals are wrong.
10

Coordination and service handoff

Engineering core
+
Topics
•Single versus multiple agents
•Handoff contracts
•Shared state ownership
•Concurrency
•Deployment limits
•Operator runbook
Hands-on lab
Compare one bounded specialist handoff with the single-workflow baseline and test conflicting updates.
Failure scenario you solve
Two workers independently act on the same record or disagree about task ownership.
You build
Coordination experiment and service/runbook notes.
Assessed outcome: Justify any added coordination and handle conflicts explicitly.
11

Independent bounded-agent capstone

Capstone
+
Topics
•Frozen tools
•Policy
•State
•Evaluation
•Replay
•Restart
•Denied actions
•Failure reporting
Hands-on lab
Build an isolated maintenance agent that proposes typed actions and executes only permitted local operations.
Failure scenario you solve
A replayed or hostile request changes state twice or bypasses authorization.
You build
Agent repository, policy/tests, traces and recovery guide.
Assessed outcome: Demonstrate authorized, denied, ambiguous, replay and failure cases independently.
12

Technical interviews and portfolio defense

Career preparation
+
Topics
•Trust-boundary reasoning
•State traces
•Tool/schema review
•Incident diagnosis
•Personal contribution
Hands-on lab
Diagnose an unfamiliar policy, retry or checkpoint defect and defend the agent design.
Failure scenario you solve
A learner attributes a duplicate effect to the model without examining executor state.
You build
Architecture case study and individual assessment.
Assessed outcome: Trace proposal, authorization, execution and receipt separately.

Scope note: Core depth is a bounded, evaluated tool-using system. Multi-agent coordination is one controlled comparison, not a promise of universal autonomous enterprise operation. Durable checkpoints alone do not make remote side effects exactly once.

Tools and methods

Work with a focused, qualified stack.

Python and structured model adapterPropose actions through an explicit planner interface.
Typed schemas and policy gatewayValidate requests before execution.
SQLite or a qualified teaching databaseTrack local state, operations and receipts.
LangGraph or explicit workflow stateImplement the selected checkpoint/resume approach.
MCP teaching serverPractise a scoped tool-interface integration.
Evaluation and redacted tracesInspect decisions, mutations and recovery behavior.

The core uses isolated fixtures, a local service/database and a qualified model/workflow stack. Provider/model access and usage budgets are specified before enrollment. Tools that send messages or change real external systems are not required for the capstone.

Projects and portfolio

Three guided projects and one independent capstone.

The guided projects develop across the modules and are integrated into the core.

Guided project 1

Typed tool gateway

Validate schemas, ranges and permissions around an isolated local service.

Portfolio evidence
SchemasPolicy testsMutation checks
Guided project 2

Restart and replay workflow

Implement operation IDs, checkpoints and conflicting-payload handling.

Portfolio evidence
State storeCrash testsReceipts
Guided project 3

Agent-versus-workflow evaluation

Compare a model planner with a deterministic baseline under normal and hostile fixtures.

Portfolio evidence
Evaluation setTracesDesign decision
Capstone

A bounded maintenance agent with auditable execution

Build an isolated agent over supplied maintenance records. Separate planning from execution, enforce tool permissions, retain state and receipts, and demonstrate safe handling of denied requests, replay and interrupted execution.

What you submit
  • •A frozen task specification and acceptance checklist.
  • •Source, configuration and data/input manifests.
  • •Baseline comparisons and retained evaluation results.
  • •Tests of the required failure cases.
  • •A readable handoff and individual technical defense.
What the assessor checks
  • •Freeze allowed tools, identity rules, schemas and state transitions.
  • •Rejected or unauthorized actions do not mutate state; approvals bind to exact actions and are revalidated where necessary.
  • •Replay the same operation without duplicate local effects and reject conflicting payload reuse.
  • •Demonstrate crash/restart behavior and a documented response to uncertain remote effects in simulation.
  • •Compare against a deterministic baseline; retain normal, denied, ambiguous and hostile evaluation cases.
  • •Use isolated fixtures and a local teaching database, not real customer communications or uncontrolled external actions.
Assessment and completion

Demonstrate practical competence.

RoboEdify · Certificate of Completion
Agentic AI Engineer
Presented to
Learner name
Awarded for completing the practical assessments and independently defending the course capstone within its documented scope.
Manikanta Kona
Founder · RoboEdify
ROBO
EDIFY
CERT
30%
Module labs
20%
Practical checkpoints
35%
Capstone
15%
Individual debugging and defense
The proposed completion standard is 70% overall, at least 60% separately in capstone and individual defense, and completion of all mandatory practical requirements. Feedback identifies gaps for remediation and reassessment. A numerical score does not compensate for missing required behavior.
This is a proposed RoboEdify course credential. External vendor certification or accreditation is not implied.
Career preparation and progression

Prepare for relevant work with evidence you can explain.

Agentic AI Application Trainee / Junior EngineerBuild bounded tool-using applications and policy checks.
AI Workflow / Integration DeveloperImplement state, tool contracts and recovery behavior.
Agent Evaluation / Reliability TraineeInvestigate execution quality, failures and adversarial cases.

Your preparation includes a reviewed repository, a technical case study, truthful resume statements and a live debugging/interview exercise. Each employer sets its own experience and eligibility requirements.

Career support

Build your portfolio. Prepare your profile. Practise your interviews.

Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. The shared hiring-partner network includes Infosys, TCS, Deloitte, Accenture, Cognizant, NTT Data and Capgemini.
01 / PORTFOLIO

Evidence from your own work.

For this course, your portfolio centres on typed tools, policy tests, durable workflow traces and recovery evidence you complete.

02 / PROFILE

Profile and resume preparation.

A reviewed technical project summary, a readable repository and resume statements grounded in your contribution.

03 / INTERVIEWS

Interview practice and introductions.

Technical interview practice, with role-fit introductions where available.

RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.

Institute leadership

Meet the team behind RoboEdify.

MK
Manikanta Kona
Founder, RoboEdify · Enterprise AI Architect
Enterprise AI · Agentic Systems · LLM Platforms · Robotics & Edge AI
15 yrs
ENTERPRISE AI
2,400+
LEARNERS
4.9 /5
RATING

Manikanta brings 15 years of enterprise platform architecture experience from AT&T, Salesforce, Cox Communications and Broadcom. His background includes enterprise platform and AI rollouts for Fortune-500 banks, telcos and insurers, and production agentic-AI deployments for governed case handling.

Education: M.S. in Engineering, Purdue University.

RK
Ravi Krishna
Chief Technologist, RoboEdify · Implementation & Delivery Lead
Enterprise automation · Deployment · Evaluation evidence · Delivery governance
10 yrs
IMPLEMENTATION & DELIVERY
1,800+
LEARNERS
4.8 /5
RATING

Ravi leads RoboEdify's implementation and delivery practice. His background spans enterprise automation programs, deployment, evaluation evidence and delivery governance.

Industry voices from RoboEdify’s AI programs

What employers say about RoboEdify’s AI and robotics graduates.

The following testimonials retain their original program context. They describe AI, robotics and enterprise-program experience, rather than outcomes from this newly drafted course.

Microsoft logo

RoboEdify grads ramp 40% faster on applied AI projects than typical hires. Best AI engineering pipeline in India.

Aakash Mehta

Aakash Mehta, Partner Programme Lead, Microsoft

Deloitte logo

We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for ML plus AI agent practices.

Anita Sharma

Anita Sharma, Senior Manager, Deloitte

Mphasis logo

The programme is comprehensive — predictive ML, LLM systems, plus agentic and robotics work. Grads come pre-trained for enterprise.

Rahul Bhatt

Rahul Bhatt, Solutions Lead, Mphasis

TCS logo

Their agent + robotics track produces engineers who ship production-grade perception and control code on day one. Genuinely rare.

Deepak Pillai

Deepak Pillai, Senior Architect, TCS

Accenture logo

What sets RoboEdify apart is the simulation-to-hardware layer baked into the AI track. Our clients ask for exactly this profile.

Suresh Menon

Suresh Menon, Practice Lead, Accenture

Infosys logo

Their fundamentals prep is rigorous, and the capstone with a real deployed system and safety case is what closes interviews for us.

Vikram Iyer

Vikram Iyer, Director, Infosys

Wipro logo

RoboEdify's AI grads get models into production twice as fast in the first 90 days. Our internal metrics back this up clearly.

Lakshmi Nair

Lakshmi Nair, VP Engineering, Wipro

Cognizant logo

Best AI + robotics pipeline we've sourced from in India. Their projects are production work, not toy code.

Karthik Subramanian

Karthik Subramanian, Engineering Director, Cognizant

Capgemini logo

Strong ML and edge-deployment foundation. Their grads need almost zero ramp time on enterprise engagements with us.

Arun Joshi

Arun Joshi, Practice Director, Capgemini

IBM logo

We've placed 40+ RoboEdify alumni across our AI and automation teams. Strong fundamentals, sharp on the agent stack.

Sanjay Verma

Sanjay Verma, Talent Director, IBM

LTIMindtree logo

ITOM + Predictive Intelligence is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.

Anjali Desai

Anjali Desai, Practice Head, LTIMindtree

Tech Mahindra logo

Their AI track delivers engineers who navigate data, models and integrations on customer engagements unsupervised.

Ramesh Iyer

Ramesh Iyer, Senior Manager, Tech Mahindra

Cyient logo

Hired 25+ RoboEdify graduates for our AI practice. Strong coding, strong ML depth, sharp on the agent layer.

Geetha Pillai

Geetha Pillai, Talent Acquisition Lead, Cyient

Microsoft logo

RoboEdify grads who blend robotics with Azure OpenAI land production-ready on day one. Rare combination, well-trained.

Priya Reddy

Priya Reddy, Talent Lead, Microsoft

AI alumni across RoboEdify

Meet alumni featured in our AI programs.

SB
Spandana Bala
ML Engineer
Hyderabad · India
Now at · Infosys
NV
Naveen Vedala
AI Agent Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
Simulation Engineer
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
Robotics Software Engineer
Seattle · United States
Now at · Accenture
MM
Mujahed Mohammed
Edge AI Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
Physical AI Engineer
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Autonomous Systems Engineer
New York · United States
Now at · NTT Data
RD
Rahul Dhamma
Robot Learning Engineer
Hyderabad · India
Now at · Cognizant
Our locations

Come chat with us—on campus or online.

Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave · Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Opening hours
Mon–Sun · 7 AM–9 PM
Online
Global
Live online classes and mentorship, with working-professional learning options. This course uses the software and compute arrangements described in its tools section.
Format
Live online + mentorship
Options
Working-professional
Learning support

Support when you need to catch up.

Freeze your seat for up to 90 days and rejoin the next class at no extra fee. TAs run catch-up sessions every Saturday, and recordings of every live session are available for the lifetime of your account.

FAQs

Questions about prerequisites, tools and completion.

Do I need to complete Generative AI Engineer first?+
That course is a suitable route, but equivalent LLM/application experience is accepted through a diagnostic.
Will I learn MCP?+
Yes, through one scoped teaching integration. MCP is an interface protocol; application authorization and execution policy still need to be implemented.
Is this mainly a multi-agent course?+
The core is a reliable bounded agent or workflow. A limited coordination experiment teaches when multiple agents help and when they add complexity.
Will the agent act on real company systems?+
The assessed project uses isolated fixtures and a local service. Real integrations require separate operational authorization and review.
Does restart support prevent all duplicate actions?+
No. You must reason about replay and each side effect. Local transactions and remote operations have different guarantees.
Can graduates and working engineers both join?+
Yes, with the stated readiness. Both follow the same practical core; preparation is assigned through a diagnostic.
How is the learning workload structured?+
The program combines live classes, guided labs, projects and support sessions. An advisor can explain the current class format before enrollment.
Can I study online or on campus?+
RoboEdify offers its Hyderabad campus, live online classes and a working-professional format. The course-specific software and compute setup is explained separately.
What if I need to pause or catch up?+
You can freeze your seat for up to 90 days and rejoin the next class at no extra fee. Saturday catch-up sessions and recordings of every live session support learning. Recordings remain available for the lifetime of your account.
Is placement guaranteed?+
No. Support includes portfolio/profile preparation, interview practice and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location or timeline.
What if I miss a practical requirement?+
Feedback identifies the missing capability and reassessment work. Attendance alone does not meet the completion standard. Confirm course-specific reassessment arrangements before enrollment.
How can I ask about fees and tool access?+
Contact an advisor about preparation, the current offering, fees, software/compute access and any model-usage costs before enrollment.

Still have a question?

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Plan your learning

Course
Agentic AI Engineer
Preparation
Diagnostic-based preparation before the common core
Level
Specialist
Curriculum
12 modules

Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.