Most companies we talk to want an AI agent. When we ask what exactly the agent does, we get answers like "it handles everything", "it will communicate with suppliers, track invoices, and schedule shifts". That's where the problem lies. An agent that does everything usually does nothing reliably.
This article is a practical guide — not theory, but a decision framework we have validated in real deployments. We will walk you through choosing the first use case, defining tools, guardrails and evaluation, all the way to gradually expanding the agent. We will also tell you what to avoid at the start.
Step 1: Choose a narrow, well-scoped use case
The most common reason a first agent fails is not technical. It is scope that is too broad.
A good first use case meets these criteria:
- Clear input and output — the agent receives something specific and returns something specific (not "processes an email")
- Verifiable result — you or the system can determine whether the agent did the right thing
- Bounded tools — a maximum of 3–5 tools, not "access to all systems"
- Tolerable error rate — if the agent makes a mistake, you can catch it before it has an impact
Examples that work well as first projects in practice: an agent that every morning pulls new complaints from the helpdesk, categorises them and writes a summary to Excel or a spreadsheet; an agent that monitors new orders in the system and triggers the prescribed API steps when an order meets defined conditions; an agent that on request retrieves relevant documentation from an internal library using RAG over industrial documentation and formulates a response.
Conversely, we do not recommend as a first project: an agent with access to multiple departments at once, an agent that sends emails or issues invoices without supervision, or an agent where it is not clear how you will measure success.
Step 2: Define tools — not "what the agent can do", but "what the agent is allowed to do"
Tools are the heart of the agent. An agent without tools is just a chatbot; an agent with overly permissive tools is a security risk.
When defining tools, think in two steps:
What does the agent physically need? - Reading (database, API, files, vector DB) - Writing (update a record, send a message, write to a system) - Calculation or transformation (format conversion, summarisation, classification)
What will the agent be permitted to do? This is the critical step that gets skipped. Every tool must have an explicit scope: the agent may read records from table X but not delete them. It may call API endpoint Y with a read-only key. It may write to section Z but not modify historical records.
A good rule for the first agent: write operations only through a HITL gate — meaning with human approval before execution. Human-in-the-loop for agents is not a complex architecture; in LangGraph it is a single interrupt() call. It adds latency equal to the human response time, but prevents irreversible mistakes.
The schema of every tool must be machine-validatable. If the agent returns malformed JSON as a tool argument, you must catch it and retry — not silently ignore it. This is not an edge case; in production it is a common source of problems.
Step 3: Choose a simple architecture
For the first agent we recommend a ReAct loop — Reason, Act, Observe in a simple graph. The reason is straightforward: it is the most debuggable architecture.
If you have read the article AI agent architectures, you know that more sophisticated patterns exist (Plan-and-Execute, Reflexion, multi-agent). Those are valid — but not for the first project. Every additional step is another potential failure point and another layer where you cannot see what is happening.
Concrete recommended stack:
- Framework:
LangGraph— graph-based, explicit state, built-in checkpointing; production-proven for enterprise deployments - Model: start with a cheap, fast model (Haiku tier, Flash tier, or a local model via
Ollama); enable frontier models (Opus, GPT) only once the core logic is working - Memory: short-term context in the graph state; for a longer horizon a vector DB — but only if the first use case genuinely requires it
A common mistake at the start: choosing the most powerful and most expensive model because "we want the best results", then wondering why costs are unsustainable. The model is easy to swap. Architecture and tools are much harder.
One practical detail: set a maximum loop step count. An agent without a limit can loop on unexpected input and consume thousands of tokens overnight. A value of 10–15 steps per task is a reasonable starting point for most use cases.
Step 4: Guardrails — not as a filter, but as architecture
Guardrails for AI agents are a topic that gets underestimated on the first project and then painfully addressed later.
Guardrails are not just an output filter ("check whether the response contains something harmful"). Guardrails are a multi-layer architecture:
- 1.Input validation — what enters the agent? Is it the format the agent expects? Does the input contain potentially dangerous instructions?
- 2.Intent classification — if the agent receives free text from users, classify the intent before passing it to the agent loop
- 3.Tool permission scope — as described above: every tool has permissions
- 4.HITL gate for write operations — critical actions require human confirmation
- 5.Output filter — semantic check of the response before delivery
For a first agent in an internal environment (not exposed to external users), starting with points 3 and 4 is sufficient. Input validation and intent classification are essential when the agent receives inputs from external or unknown sources.
Prompt injection — an attack where malicious content in the input convinces the agent to do something other than intended — is, according to the OWASP LLM Top 10, the most common security threat in AI applications. If your agent reads any external content (emails, web pages, files from customers), you must actively account for this risk.
Step 5: Observability — do not deploy the agent to production without it
This is a rule we do not compromise on: an agent without observability is not a production agent. It is a proof of concept.
What you specifically need to know about every agent run:
- What the input and output were
- How many loop steps were executed
- Which tools were called, with what arguments, and what they returned
- Where an error occurred (if it did) and why the agent chose a particular action
Tools that provide this: Langfuse (framework-agnostic, self-hostable), LangSmith (deep integration with LangGraph), Arize Phoenix (OpenTelemetry-based, advanced eval metrics). AI agent observability is a mature area today — there is no reason to build it from scratch.
A concrete example from practice: in a deployment for a logistics client, we discovered through traces that the agent correctly categorised 94% of cases, but consistently called one tool with an incorrect argument for one particular category. Without observability, we would have found out only after dozens of incorrect records had been written to the system.
Step 6: Eval — how do you know the agent is working correctly?
Eval (evaluation) is the step most commonly skipped on the first project. The consequence: we do not know whether a change to the prompt, model, or tools made things better or worse.
For the first agent a simple eval harness is enough:
- Compile 20–50 test cases with a defined input and expected output or action
- Run the agent over these cases after every significant change
- Track at least two metrics: task completion rate (did the agent complete the task?) and tool call accuracy (did the agent call the right tools with the right arguments?)
You do not need to deploy a complex eval framework right away. A script that runs the agent over a test set and prints a score is better than no eval at all. As the system grows, evaluating RAG and LLM applications becomes more sophisticated — but the foundation remains the same: have a reference set and measure against it.
What to avoid with the first agent
From deployment experience — here are the most common mistakes that extend a project or stop it altogether:
Too many tools from the start. Every tool is another source of failure. Start with three, expand based on actual need.
No HITL for write operations. The first write operation the agent performs incorrectly with no way to catch it usually kills the project. Add approval — you can remove it later once you have confidence in reliability.
A multi-agent system as the first step. A multi-agent system in practice makes sense when one agent is not enough. But when you do not yet know what a single agent is doing wrong, adding more agents increases the confusion, not reduces it.
The model as the main variable. We have heard this more than once: "We'll try GPT-5, that will definitely solve the problems." In practice, most problems stem from poorly defined tools, missing validation, or an unclear scope — not from model quality. Fix the architecture, not the model.
No maximum limits. An agent without a step count limit, without a timeout, and without a cost alert can generate thousands of calls overnight. Set limits from day one.
Gradually scaling up: from prototype to production
The first working agent is a prototype. The path to production requires several explicit steps:
- 1.Eval over a test set — at least 20–50 cases, task completion rate > 85% as a minimum condition
- 2.Shadow mode — the agent runs in parallel with the existing process but does not intervene; you compare outputs
- 3.HITL for all write operations — in the first production phase, a human approves every action
- 4.Observability live — traces must be available from day one in production
- 5.Gradually reducing HITL — after 2–4 weeks with good metrics you can remove HITL for low-risk actions
Expanding scope (new tools, new use cases) is always an iteration of the same cycle: define, test in shadow mode, gradually deploy, monitor metrics.
Frequently asked questions
What model should I choose for the first agent?
Start with a cheap and fast model — Haiku or Flash tier, or a local open-weight model such as Ollama with Qwen or Llama. Enable frontier models (Opus, GPT) only once the core logic is working and you know exactly where the model falls short. Swapping the model is easy; fixing the architecture is not.
How many tools can the first agent have?
We recommend a maximum of 3–5 tools in the first version. Every tool is another source of failure and another thing that needs to be tested. It is better to have three well-tested tools than ten with unclear boundaries.
Do I have to use LangGraph or another framework?
It is not mandatory. Custom code gives full control and sometimes less abstraction than is needed. The reason we recommend LangGraph for first production deployments is built-in checkpointing, HITL interrupt(), and good traces — these are things you otherwise have to build from scratch.
When does it make sense to move to a multi-agent architecture?
When one agent consistently cannot keep up — typically because the task scope is too large or parallel processing is needed. If the agent performs 15 sequential steps where some could run in parallel, it is time for a multi-agent approach. But this is the second or third project, not the first.
How do you estimate the costs of an agent in production?
The key variable is not just the model price, but also the number of loop steps per task and the retry rate. If the agent averages 5 steps and the retry rate is 12%, the actual number of calls is 12% higher — and in multi-stage pipelines this overhead compounds further. A more detailed framework for cost estimation can be found in the article AI agent costs in production.
*MP Industrial Solutions helps companies define, design and deploy their first AI agent — from selecting the use case through technical architecture to production deployment with observability and guardrails. If you are considering your first step, we are happy to assess your specific case in an initial consultation.*