HIGAET Agentic AI Engineering
Design autonomous agents with planning, memory, and tools, covering orchestration, multi-agent collaboration, and guardrails through hands-on engineering projects.
Duration
10 weeks · 6-8 hours/week
Level
Advanced
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
Agentic AI engineering is the discipline of building autonomous agents that can plan a task, use tools, remember context, and work through multi-step goals with guardrails. It matters now because teams are moving past single chat replies toward systems that can research, act, and collaborate to complete real workflows.
Support teams, operations staff, and retailers use agents for research, triage, and coordination work where a single prompt is not enough. Agents solve multi-step execution, task decomposition, and collaboration between specialized roles, but they do not fix unclear goals, missing permissions, or untrustworthy data — an agent with no guardrails or bad inputs still fails.
By the end you will be able to build a single agent with planning and reflection loops, a multi-agent system with defined roles and handoffs, and a persistent-memory assistant backed by state stores and conversation history.
Why this course exists
The gap is between a toy demo that answers one question and a production agent that plans, remembers, uses tools, and stays within guardrails across long runs. This course teaches the arc from Model and Prompt through Context, Tools, and Agents to Evaluation, Security, Infrastructure, and Production, so students can orchestrate reliable multi-agent systems.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Familiarity with LLM APIs and prompts
- Basic understanding of state stores and queues
Technologies & tools
Skills you'll gain
A 10 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Agent loops, planners, tools, and memory models
- Module 02
Module 02 — Core: Tool design, schemas, error handling, and retries
- Module 03
Module 03 — Core: Memory systems, state management, and context budgets
- Module 04
Module 04 — Engineering: Single-agent task automation workflows
- Module 05
Module 05 — Engineering: Multi-agent collaboration and supervisor patterns
- Module 06
Module 06 — Advanced: Browser, code, and data tools for agents
- Module 07
Module 07 — Production: Guardrails, approvals, observability, and cost control
- Module 08
Module 08 — Capstone: Ship a multi-agent system for a defined operations task
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Architect single and multi-agent systems with defined roles and handoffs
- Build planning, reflection, and task-decomposition loops for agents
- Develop persistent memory using state stores and conversation history
- Integrate browsers, code runners, databases, and custom tools
- Deploy agent services with job queues, retries, and human approval gates
- Evaluate task success, tool accuracy, and failure recovery paths
- Secure agent actions with scopes, allowlists, and audit trails
- Optimize step counts, context size, and execution cost
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Tool-using planning agent
- Project 02
Reflection and task-decomposition loop
- Project 03
Multi-agent collaboration system
- Capstone
Guardrailed multi-agent orchestration platform
Speak the language first.
- Agent planning loops
- Step-by-step reasoning cycles where an agent breaks a goal into tasks, acts, and revises its plan based on results.
- Task decomposition
- Splitting a complex goal into smaller subtasks that an agent can execute in order or in parallel.
- Agent tools
- External functions such as search, calculators, or APIs that an agent can call to act beyond text generation.
- Agent memory
- Stored conversation history and state that lets an agent recall past steps and user context.
- Multi-agent orchestration
- Coordinating several agents with defined roles so work passes cleanly between them.
- Role handoffs
- Rules for passing tasks and context from one agent to another without losing information.
- Reflection
- A self-check step where the agent reviews its own output for errors before proceeding.
- Guardrails
- Limits on agent behavior, such as allowed tools and approval gates, that keep actions safe and on scope.
- State stores
- Databases or key-value stores that hold agent progress so long tasks can pause and resume.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Agent loops without finishing a task
Add a maximum step count and a stop condition, then check the plan log to find the repeating step and tighten its completion criteria.
Handoffs between agents lose context
Pass a structured handoff object with goal, prior results, and next action, and log each handoff to confirm nothing is dropped.
Agent calls the wrong tool or wrong arguments
Simplify tool descriptions and validate arguments against a schema before execution, then review failed calls to clarify ambiguous names.
Memory grows too large and slows the agent
Summarize older conversation turns into a compact state record and keep only recent steps in the active prompt.
Agent takes unsafe or out-of-scope actions
Restrict the tool allowlist, add approval gates for sensitive actions, and test guardrails with adversarial prompts.
Before you move on, you should be able to
- Design single and multi-agent systems with defined roles and handoffs
- Build planning and task-decomposition loops for autonomous goals
- Develop persistent agent memory with state stores and history
- Evaluate multi-agent collaboration for correctness and efficiency
- Deploy agents with guardrails and orchestration controls
- Explain how planning, memory, and tools combine in agent behavior
- Build reflection steps that catch and correct agent errors
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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Ready to start HIGAET Agentic AI Engineering?
A 10 weeks course — AI & Generative Intelligence.