AI Agent Development
Build autonomous AI agents that plan, use tools, remember, and run reliably in production — not demos that break on the second turn. A 12-week, project-first program for fresh IT graduates and working professionals who want to ship multi-agent systems, MCP integrations, and voice and browser agents that survive real users.
The AI Agent Development program is a live, instructor-led deep-dive into building autonomous systems that plan, use tools, remember, collaborate, and run unattended in production. Every module is built around something you ship: a support agent that escalates to humans, a custom MCP server, a supervisor-led multi-agent system, a voice agent that handles interruptions — not slides about them.
It is designed for two audiences at once. If you are a fresh IT graduate, it gives you a portfolio of deployed agents that speaks louder than any resume line. If you are a working professional — Python, backend, data, ML, or automation — it turns "I can call an LLM API" into "I can ship an agentic system that handles errors, scales, and survives real users."
The program runs 12 weeks (~90 hours), live online and project-first, and closes with a capstone reviewed 1:1: you design, build, evaluate, deploy and defend one production-grade multi-agent system end-to-end.
Is This the Right Course for Me?
- Generative AI & LLM Engineering — the broad foundation: models, prompting, RAG, fine-tuning and LLMOps across the whole GenAI stack.
- AI Agent Development (this course) — the specialist deep-dive: autonomous systems that plan, use tools, remember, collaborate, and run unattended in production.
Course at a Glance
- Duration: 12 weeks | ~90 hours | live + hands-on
- Format: Live online · project-first · capstone reviewed 1:1
- Prerequisite: Working Python and basic familiarity with any LLM API. No ML/maths background required.
- Curriculum: 10 modules, from agent design patterns to deployment and capstone
- Portfolio: 7 production-style agent projects plus a defended capstone
Course Highlights
- Every module leads with what you build, not what you "learn about"
- Graph-based agents with LangGraph: durable execution, interrupts, human-in-the-loop
- Model Context Protocol (MCP) servers and clients as the standard tool interface
- Multi-agent patterns — and the failure modes nobody warns you about
- Specialized agents: retrieval, text-to-SQL, browser/computer-use, voice, background
- Reliability engineering: trajectory evals, tracing, guardrails, OWASP LLM Top 10
- Framework fluency without lock-in: OpenAI Agents SDK, CrewAI, AutoGen and more
Hands-on Projects & Portfolio
RAG-backed knowledge, issue classification and automatic escalation to a human.
Internal tools and databases exposed to a customer-facing agent through one standard interface.
Parallel specialist agents coordinated by a supervisor into one research-and-report pipeline.
Answers business questions conversationally against a real database.
Completes a real multi-step task end-to-end by operating a live browser.
Realtime speech with tool access and interruption handling.
Trajectory scoring, nightly regression runs and a cost dashboard.
Tools & Stack
Python LangChain LangGraph LangSmith OpenAI Agents SDK CrewAI AutoGen LlamaIndex MCP A2A Anthropic Claude OpenAI Gemini Pydantic pgvector Qdrant Mem0 Langfuse E2B Playwright FastAPI Docker Redis Temporal
Career Outcomes
Agentic systems are where hiring demand has moved fastest, and agent orchestration plus eval design are now the strongest differentiators on an AI engineering resume. This course targets the roles at the centre of that shift:
- AI Agent Engineer
- Agentic AI Engineer
- Applied AI Engineer
- AI Automation Engineer
- AI Solutions Architect
- LLM Engineer
- AI Platform Engineer
Who This Course Is For
- Final-year students and fresh IT graduates targeting an AI-first first role
- Python, backend and full-stack developers who can call an LLM API but can't yet ship a system that handles errors, scales, and survives production
- Data scientists and ML engineers moving from models into autonomous systems
- Automation, RPA and integration engineers upgrading rule-based workflows to agentic ones
- Tech leads and architects who must make build-vs-buy calls on agent platforms
Material Includes
- Class recordings
- Lab guides & project starter repos
- Eval datasets & grading rubrics
- Certificate
Prerequisites
- Working Python
- Basic familiarity with any LLM API
- No ML or maths background required
Estimated Time: 8 Hours
You build:- The same task three ways — prompt chain, workflow, and autonomous agent — with a decision memo on which one production actually needs
- What actually makes a system "agentic"; the spectrum from prompt chain to workflow to autonomous agent
- ReAct, plan-and-execute, reflection and self-critique loops, router patterns
- Critically: when not to use an agent
Estimated Time: 8 Hours
You build:- A tool-calling agent with Pydantic-validated outputs, parallel tool calls, retries, and sandboxed code execution via E2B/Modal
- Function/tool calling; JSON schemas and Pydantic validation
- Tool design that models can actually use; error handling and retries; parallel tool calls
- Sandboxed code execution (E2B / Modal)
Estimated Time: 8 Hours
You build:- A custom MCP server exposing internal systems and a database as agent tools, with auth, plus a client that consumes third-party servers
- Building MCP servers and clients; transport options; auth
- Exposing internal systems and databases as agent tools
- Replacing bespoke integration layers with one standard interface; publishing and consuming third-party servers
Estimated Time: 8 Hours
You build:- An agent with checkpointed state and long-term semantic memory that survives restarts and remembers users across sessions
- Short-term vs long-term memory; conversation state; TypedDict and Pydantic state schemas; reducers
- Checkpointing and persistence; semantic memory with vector stores; Mem0
- Context-window management and compaction
Estimated Time: 10 Hours
You build:- A durable graph-based agent with conditional routing, streaming, human-in-the-loop approval gates, and time-travel debugging
- Graph-based agents with nodes, edges and conditional routing; subgraphs
- Durable execution; streaming; interrupts
- Human-in-the-loop approval gates; time-travel debugging
Estimated Time: 10 Hours
You build:- A supervisor-led research-and-report system with parallel specialist agents, shared state, and quality loops that actually converge
- Supervisor and swarm/decentralized patterns; task delegation; message passing; shared state
- Agent-to-agent (A2A) communication; convergence and quality loops
- The failure modes nobody warns you about: infinite loops, cost blowouts, responsibility diffusion
Estimated Time: 6 Hours
You build:- One agent ported across two frameworks, plus a build-vs-buy evaluation matrix your team can reuse
- OpenAI Agents SDK, CrewAI, AutoGen/AG2, LlamaIndex Workflows, Google ADK, AWS Strands
- What each is good at; how to choose
- How to avoid framework lock-in
Estimated Time: 12 Hours
You build:- Four working agents: a retrieval agent over enterprise knowledge, a text-to-SQL agent, a browser-operating agent, and a realtime voice agent with barge-in
- Retrieval agents over enterprise knowledge; text-to-SQL database agents
- Browser and computer-use agents
- Voice agents: STT/TTS, realtime APIs, barge-in and latency budgets
- Scheduled and event-driven background agents
Estimated Time: 10 Hours
You build:- An agent eval harness with trajectory scoring, a calibrated LLM-as-judge, CI regression runs, full tracing, and a hardened guardrail layer
- Trajectory and task-completion evals; LLM-as-judge with calibration; regression suites in CI
- Tracing with LangSmith / Langfuse / Phoenix; cost and latency dashboards
- Guardrails; prompt injection and indirect injection defense; OWASP LLM Top 10; least-privilege tool permissions; audit trails
Estimated Time: 10 Hours
You build:- One production-grade multi-agent system, end-to-end: designed, built, evaluated, deployed — and defended in a 1:1 review
- FastAPI serving; containerization; durable long-running orchestration
- Concurrency and rate limits; cost control at scale; versioning agents safely
- Capstone design review, deployment, and 1:1 defense
- What's App: +91 73-01-11-21-31 for immediate assistance during business hours.
- Email: Send your inquiries to hello@progmatrix.com, and one of our representatives will get back to you as soon as possible.
- Website: You can also use the contact form on our website to send us a message directly.