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Agentic AI Architecture

Between an agent and its tools sits the Model Context Protocol (MCP); between agents from different vendors, the Agent2Agent protocol (A2A); and in front of remote MCP servers, the specification describes authorization through OAuth. Which agent acts with which identity and which permissions is your decision. In Agentic AI Architecture you design the target architecture for it, with orchestration, approvals and platform strategy.
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What this module delivers.

From one agent's autonomy level to the company's platform strategy: in the tecnovy Agentic AI Architecture training you design a target architecture that spans both, on a case study. You set identity, permissions and approvals for every agent.

How the training runs

Method
Architecture workshops on a case study, assessing design variants
Basis
Vendor-neutral tecnovy curriculum using open standards like MCP and A2A
Outcome
tecnovy certificate of participation, no examination

Dates & booking

Choose a date that fits

No public dates are scheduled yet.

New dates are published regularly. Join the waitlist or plan an in-house course for your team.

Request in-house training

Fit

Who this module is designed for

Typical roles

  • You work as an enterprise, solution or software architect and need to plan agents into the system landscape.
  • You help decide whether your company builds, buys or combines an agent platform.
  • You are responsible for IT security or AI governance and decide what agents are allowed to do.

Prerequisites

No prerequisites

You can start right away. What you need: experience in architecture work, basic knowledge of AI agents.

Consider instead AB-100 For architects on Microsoft platforms who design agentic business solutions with Copilot Studio, Microsoft Foundry and Dynamics 365 and prepare for Microsoft's AB-100 exam.

Curriculum

Agentic AI Architecture in Detail

Five parts lead from the patterns of agentic systems, through orchestration, integration and identities, to governance and platform strategy. One case study connects them: on it you draft design variants, assess them and settle on a target architecture at the end.

01Understanding agentic systems

You work out how much autonomy an agent should get in a given use.

  • Levels of agent autonomy
  • Architecture patterns and their limits
  • Use cases in the enterprise
02Orchestration and collaboration

You decide how agents are coordinated and how they work together.

  • Central orchestration through a coordinating agent
  • Decentralised collaboration between peer agents
  • Collaboration through standards such as A2A
03Integration

You plan how agents get to systems and knowledge.

  • System access through APIs
  • Tools and data through the Model Context Protocol (MCP)
  • Knowledge sources and data rights
04Security and identity

You set who an agent acts as and what it may do.

  • Identities for agents
  • Minimal permissions instead of blanket access
  • Protection against prompt injection and data leakage
05Governance and operation

Finally you settle how agents are approved, how their actions are evidenced and which platform they run on.

  • Approval processes
  • Logging and traceability
  • Platform strategy: build, buy or hybrid

Outcome

What you will be able to do afterwards

  1. 01

    You classify use cases in your company by level of autonomy and name the limits of agentic systems.

  2. 02

    You design a target architecture for agentic systems on a case study and assess design variants.

  3. 03

    You choose between central and decentralised orchestration and justify the choice.

  4. 04

    You plan how agents reach systems through APIs and MCP and work with other agents through A2A.

  5. 05

    You set which knowledge sources an agent may use and which data rights apply.

  6. 06

    You define identities with minimal permissions for agents and plan protection against prompt injection (injected instructions) and data leakage.

  7. 07

    You design approval processes and logging that make agent decisions traceable.

  8. 08

    You compare build, buy and hybrid and derive a platform strategy for agents.

Offer scope

What is included

Included

  • Reference architecture for agentic systemsYou take it away as a starting point for the target architecture in your company.
  • Governance checklist for agentsIt collects the checks from the governance and operation part.

Why tecnovy

What you get on top with us

01

No Slideshow, Hands-On!

Promised: no PowerPoint marathon. We work in groups, tie theory to practice, and you get real project examples from our experienced trainers plus the exchange with like-minded people.

02

Certificate Showroom

Get your certificate of participation and, if you have one, add your exam certificate from E-Learning. Fully automated, beautifully designed. Just for you, only at tecnovy.

03

Flexible Date Change

If you are not able to attend the course, you can rebook your training free of charge up to one week before the start of the training.

04

Attend Twice, Pay Once

You are welcome to attend the training online again within a year as a refresher.

05

Learn from Experts

We always guarantee you the use of didactically and methodically first-class qualified trainers who draw their knowledge from training experience as well as professional practical and project experience.

FAQs

Frequently asked questions

01Is Agentic AI Architecture vendor-neutral?
Yes, the tecnovy Agentic AI Architecture training is tied to no platform and builds on open standards such as MCP and A2A. It shows implementations on several platforms, so you can compare design variants.
02What is the Agent2Agent protocol (A2A)?
A2A is an open standard through which agents from different frameworks and vendors work together without exposing their internal tools or memory. Google developed it and donated it to the Linux Foundation, it has been part of the Agentic AI Foundation since August 2026, and the current version is 1.0.0 (as of November 2026). In the tecnovy Agentic AI Architecture training you plan where A2A carries the collaboration of your agents.
03Do we need MCP or A2A?
It depends on what is being connected: MCP links an agent to tools and data, A2A links independent agents to each other, and the A2A project describes the two as complementary, not competing. In the tecnovy Agentic AI Architecture training you decide for every connection in the target architecture which route carries it: API, MCP or A2A.
04What role does OAuth play in agent permissions?
For remote MCP servers, the MCP specification (revision 2026-07-28) describes authorization based on OAuth 2.1, which is still a draft at the IETF, and a token must be issued for the exact server that accepts it. A2A uses established schemes, among them OAuth 2.0, OpenID Connect and mutual TLS (as of November 2026). In the tecnovy Agentic AI Architecture training you build on this to set identities and minimal permissions for your agents.
05Should we build or buy an agent platform?
There is no blanket answer: in the tecnovy Agentic AI Architecture training you compare building, buying and a hybrid of both on the case study and justify a platform strategy. Choosing models and providers for individual AI solutions is covered in the tecnovy AI Solution Architecture training.
06How does Agentic AI Architecture differ from the AI Agents Deep Dive?
In the tecnovy AI Agents Deep Dive you develop a multi-agent system and check it with evaluation, attack tests and monitoring. Agentic AI Architecture works one level higher: you design the target architecture for the agents of a whole company, and on the agent side basic knowledge is enough, for example from AI Agents Foundations.
07Does Agentic AI Architecture end with an examination?
No, the tecnovy Agentic AI Architecture training ends without an examination and prepares for no vendor exam. If you attend at least 80% of the training time, you receive the tecnovy certificate of participation.
08Which certification fits after Agentic AI Architecture?
Agentic AI Architecture itself leads to no certification. If you want one, tecnovy offers trainings for Microsoft AB-100 and NVIDIA NCP-AAI. Those exams belong to Microsoft and NVIDIA and carry their own fee, and each awards the certification under its own rules.

What does your training at tecnovy look like?

Agentic AI Architecture

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