How to create an AI agent: clear architecture, tools, and typical errors
Content:
- The short answer is: what does an AI agent consist of?
- What is considered an agent and what is not
- What the agent consists of: a single-screen diagram
- Six steps of creating an AI Agent
- What to do: an overview of the tools and the rule of choice
- MCP, A2A and Skills: three standards that will be asked about
- How to understand that an agent is working: quality control
- Nine mistakes that end projects.
- How long does it take and what does the customer get?
- What to do next
The short answer is: what does an AI agent consist of?
model + tools + knowledge + management cycle + access and control rules
narrow
what kind of task should he solve, where does his powers end, and how will we know that he is coping?
What is considered an agent and what is not
one model callworkflowThe agent
For businesses, this distinction is more important than it seems. A lot of the tasks that an "agent" wants to solve are actually closed with one good model call or a neatly assembled workflow. And that's the good news: such solutions are cheaper, more transparent, faster to implement, and easier to debug. It is no coincidence that many vendors recommend starting from the bottom, rather than immediately building an autonomous system with a large number of degrees of freedom.
What the agent consists of: a single-screen diagram
The AI Agent architectureModelThe management cycle
ToolsKnowledgein the retrieval or RAG formatMemoryLimiters and observability
prompt engineeringcontext engineering
- Model
- The management cycle
- Tools
- Knowledge
- Memory
- Limiters and logs
Six steps of creating an AI Agent
The first step is to outline the task.2-8 tools
The second step is to collect real data.30-50 real-world examples
Step three is to describe the boundaries.
Step four is to gather knowledge and tools.whatwhen
Step five is to prepare the test kit before the launch.
Step six is to start in a narrow area and expand gradually.
What to do: an overview of the tools and the rule of choice
ready-made platformswhat level of difficulty does your task really need?
Ready-made platformsVisual solutionsCode frameworksDevelopment from scratch
a graph with a saved state
10-15 times more tokens
MCP, A2A and Skills: three standards that will be asked about
MCP
A2ASkills
One conclusion is enough for the business reader: the ecosystem is moving towards open protocols, which means that tools, knowledge, and even some of the logic are becoming less dependent on a single vendor. This does not eliminate the differences between the platforms, but it reduces the risk of building a system that cannot be migrated or scaled later. And that's why today it makes sense to think not only about the model, but also about the way to connect the rest of the circuit.
How to understand that an agent is working: quality control
Perhaps this is the most important and most underrated section. Many teams sincerely believe that the quality of an agent can be assessed "by feeling": they ran the script several times, saw that the answers looked reasonable, and concluded that the system was ready. Unfortunately, this is how solutions get into production that speak beautifully, but regularly make wrong decisions, do not notice tool failures, or confidently promise something that does not exist.
30 test cases100 or more
max_steps3–8
a full run of checks with each model change
The rule of thumb:
Nine mistakes that end projects.
The first mistake is that the task is too broad.
The second mistake is premature multi—agency.
The third mistake is too many tools.
The fourth mistake is the lack of a test set.
The fifth mistake is a poor knowledge base.
The sixth mistake is the lack of escalation.
The seventh mistake is unlimited recording rights.
The eighth mistake is swollen promptness and hardwired logic.
The ninth mistake is uncontrolled consumption.
The AI agent project does not die from a lack of intelligence of the model, but from the lack of a framework.
How long does it take and what does the customer get?
A few weeks
At the exit, the customer should receive not just a "bot that answers something," but a set of artifacts: a description of the task and boundaries, a map of tools, a knowledge structure, a test kit, escalation rules, a pilot scenario, and acceptance criteria. If these artifacts are not present, it is difficult to scale the project, transfer it to another team, or even evaluate it soberly. In fact, the mature development of an AI agent is both an engineering job and a package of manageability.
That is why it is useful to look at materials about the terms of reference, development stages and cost separately: they help to move the conversation from the "make us a smart agent" mode to the clear project mode with stages, risks and measurable results. Otherwise, the customer is not buying the system, but the expectation.
What to do next
from the analysis of the script itselfthe scope of work, deadlines, budget fork, and recommendations on where to narrow down the task.
If you want to try to build the first script yourself, it makes sense to start with a ready-made platform or a simple workflow, rather than with a heavy custom architecture. This will give a more honest picture of where the agent cycle is really needed, and where accurate automation is enough. As a result, you will either get a working launch faster, or you will come to development with a much more mature understanding of the requirements.
Автор
Антон Амосов
