Learn a clear, step‑by‑step manual workflow to design, build, test, and deploy your first LLM agent, including choosing the right model, structuring prompts, defining tools, and avoiding common pitfalls so you can go from idea to a working AI assistant with confidence.

Large Language Model (LLM) agents have rapidly moved from research labs into everyday products, but the fastest way to ship something useful is to follow a simple, repeatable workflow rather than chase every new framework. This guide walks you through a practical, tool‑agnostic process you can use in 2025 to build your first LLM agent manually—before you automate or scale it.

Developer designing a workflow for an AI agent on a laptop with notes
A clear manual workflow helps you design better LLM agents before introducing complex tooling.

What is an LLM Agent in 2025?

An LLM agent is an application that uses a language model not just to generate text, but to:

  • Understand user goals (via natural language input).
  • Plan steps to reach those goals.
  • Call tools or APIs (search, databases, CRMs, internal systems).
  • Iterate based on intermediate results.
  • Return a final, human‑friendly answer or action.
In simple terms: a “chatbot” responds in one shot; an “agent” can think, use tools, and act across multiple steps.

The manual workflow below treats your agent like a small software system—with requirements, data, tools, and tests—instead of just a clever prompt.


Step 1: Define a Narrow, Valuable Use Case

Most first‑time agent projects fail because they try to “do everything.” Start narrow so that:

  • Success is obvious and measurable.
  • Edge cases are manageable.
  • You can validate value quickly with real users.

Use this mini‑checklist when picking your first use case:

  1. High text friction: Lots of repetitive reading, writing, or searching.
  2. Accessible data: The agent can access everything it needs via files, APIs, or a knowledge base.
  3. Clear success metric: E.g., “reduce time to draft a customer email from 10 minutes to 2 minutes.”
  4. Low risk: No irreversible decisions without human review (especially at the beginning).

Examples of good starter agents:

  • Customer support triage assistant that drafts replies based on your help center docs.
  • Internal “policy explorer” that answers employee questions using HR and IT documentation.
  • Research summarizer that reads a set of URLs or PDFs and produces structured briefs.

Step 2: Map the Manual Workflow Before You Add AI

Before involving an LLM, write down how a human expert would complete the task step by step. This becomes your agent’s blueprint.

For each step, note:

  • Inputs: What information is needed?
  • Action: What does the human actually do?
  • Tools: Websites, internal systems, spreadsheets, etc.
  • Decision criteria: How do they know they’re done or what to do next?
  • Output format: Email, summary, JSON, ticket note, etc.

Example for a customer support email draft agent:

  1. Read the customer message and identify their main problem and urgency.
  2. Search the help center or internal docs for relevant articles.
  3. Pick the best 1–3 articles and skim key steps or limitations.
  4. Draft a response:
    • Empathetic opening.
    • Direct answer.
    • Step‑by‑step instructions.
    • Links to docs.
    • Sign‑off and next‑step options.
  5. Double‑check tone and factual accuracy before sending.

Each bullet above can later become part of a system prompt, tool call, or evaluation check in your agent.


Step 3: Choose the Right Model and Hosting Strategy

As of late 2025, you have three main options for powering your agent:

  • Hosted API models (e.g., OpenAI, Anthropic, Google, Cohere):
    • Fastest to start, no infra work.
    • Great for experimentation and low‑volume use.
  • Hosted open‑weight services (e.g., Together, Fireworks, Perplexity APIs for models like Llama, Mistral):
    • Competitive performance and cost.
    • More flexibility in choosing specific models.
  • Self‑hosting / on‑prem (e.g., Llama, Mistral on your own hardware or VMs):
    • Maximum control and data locality.
    • More ops and MLOps complexity.

For a first agent, default to:

  • Hosted API for low risk / early testing.
  • Mid‑sized, high‑context model (e.g., 32k–200k tokens context) so you can include instructions and retrieved documents.
  • JSON‑mode or structured output features if your agent needs to emit machine‑readable data.

Step 4: Design a Robust System Prompt

The system prompt functions as your agent’s job description, playbook, and guardrails. Derive it directly from your manual workflow.

Include these elements:

  • Role & audience: Who are you? Who are you helping?
  • Scope: What you can and cannot do.
  • Step‑by‑step behavior: Mirror your manual workflow steps.
  • Tone & format rules: Structure, headings, JSON schemas.
  • Safety & escalation: When to ask for clarification or defer to a human.
You are an AI customer support drafting assistant for ACME SaaS.
Your goal is to draft clear, empathetic email replies for human agents to review and send.

Follow this process:
1. Read the customer's message and identify:
   - main problem
   - product area
   - urgency (low/medium/high)
2. Search the provided knowledge base snippets. Never invent policies.
3. If relevant information is missing, ask a single clarifying question.
4. Draft an email with this structure:
   - short empathetic opening
   - direct answer in 1–3 sentences
   - numbered step-by-step instructions
   - links to 1–2 relevant help center articles (from the snippets only)
   - closing that offers next steps
5. If the request is about billing disputes, account deletion, legal, or safety:
   - do NOT answer
   - instead, say you will escalate to a human agent.

Always respond in the same language as the customer.
Keep the draft under 250 words unless the user explicitly asks for more detail.

Iterate on this prompt based on observed failures during testing; your system prompt is a living document.


Step 5: Define Tools (Functions) and When to Use Them

Tools (often called “functions” or “actions”) let your agent interact with the outside world: search, databases, CRMs, internal APIs, and more. Your manual workflow tells you which tools you need.

Common first tools:

  • Search / retrieval: Query docs, websites, knowledge bases.
  • CRUD operations: Create/read/update records in a CRM, ticketing system, or database.
  • Calculators: Handle prices, dates, or metrics reliably.

For each tool, specify:

  • Name (clear and descriptive).
  • Arguments with types, constraints, and examples.
  • What it returns and how the agent should use it.
Tool: search_kb
Description: Search ACME's help center articles by query and product area.
Arguments:
- query (string): natural language search query.
- product_area (string, enum): ["billing", "analytics", "teams", "workspace"]
Returns:
- articles (array): each with {title, url, snippet}

Agent usage:
- Call this tool whenever you cannot confidently answer from the user's message alone.
- Cite at most 2 returned articles in your draft, using their title and url.

In manual experiments, log every tool call and check whether it was appropriate. This informs better tool‑use instructions in your system prompt.


Step 6: Implement a Simple Manual Orchestration Loop

Before adopting a full‑blown agent framework, build a minimal “orchestrator” that:

  1. Sends user input + system prompt to the model.
  2. Reads the model response:
    • If it’s a tool call, execute it, append the result to the conversation, and ask the model to continue.
    • If it’s a final answer, return it to the user.
  3. Repeats until a stopping condition is met (e.g., max 3 tool calls or explicit “DONE”).

In pseudocode:

messages = [system_prompt, user_message]
for step in range(MAX_STEPS):
    response = call_llm(messages, tools_schema)
    if response.type == "tool_call":
        tool_result = execute_tool(response.tool_name, response.tool_args)
        messages.append({"role": "tool", "name": response.tool_name, "content": tool_result})
    else:
        # final answer
        messages.append({"role": "assistant", "content": response.content})
        break

This “manual” loop keeps your logic transparent and debuggable. Once you’re comfortable, you can migrate to a higher‑level framework if needed.


Step 7: Structure Inputs and Outputs for Reliability

Unstructured chat is fragile. For agents that must plug into systems, enforce structure where possible.

Input best practices:

  • Wrap user input with brief context:
    • Who the user is (role, plan, permissions).
    • Where the request came from (page, product area).
  • Include relevant retrieved data (docs, records) in a clearly delimited section.

Output best practices:

  • When possible, request JSON output that matches a schema.
  • For human‑facing text, define headings, bullet formats, and length limits.
  • Use the model’s native structured output / JSON mode if available.
You MUST respond in JSON only, matching this schema:

{
  "summary": string,
  "sentiment": "positive" | "neutral" | "negative",
  "urgency": "low" | "medium" | "high",
  "needs_human": boolean,
  "suggested_reply": string
}

Validate outputs in your code and handle failures gracefully (e.g., retry with higher temperature off and clearer schema reminders).


Step 8: Test Manually With Realistic Scenarios

Before you ship, run a manual test suite. Do not skip this step—your first agent will reveal many unexpected behaviors.

Create a simple spreadsheet or JSON file with:

  • Input: User message and context.
  • Expected behavior: In natural language (not necessarily exact text).
  • Important constraints: E.g., “must not touch billing.”
  • Pass/fail column: For reviewers to mark.

Cover:

  • Happy paths (common, straightforward tasks).
  • Ambiguous inputs requiring clarifying questions.
  • Edge cases, outdated docs, missing info.
  • Safety‑sensitive requests where the agent should escalate.

Use this feedback to refine your system prompt, tool definitions, and orchestration logic before involving a larger user base.


Step 9: Add Guardrails, Observability, and Human‑in‑the‑Loop

Production agents need safety and monitoring just like any other system. For a first deployment, keep safeguards straightforward but explicit.

Guardrails:

  • Scope filters: Reject or escalate requests outside allowed topics.
  • Policy snippets: Provide key business rules in the prompt; never rely on model memory alone.
  • Safe defaults: When unsure, ask for clarification or hand off to a human.

Observability:

  • Log prompts, responses, tool calls, and user feedback (with privacy in mind).
  • Track simple metrics:
    • Adoption (how many users try it).
    • Deflection (how often humans must step in).
    • Time saved per task.

Human‑in‑the‑loop:

  • Start with draft‑only mode where humans review and edit outputs.
  • Collect thumbs‑up/down ratings and comments directly in the UI.
  • Use problematic outputs as new test cases and training examples for future tuning.

This combination allows you to iterate safely while still delivering value from day one.


Step 10: Decide When to Automate the Workflow

Once your manual agent workflow is stable and delivering value, you can look at automation and scaling:

  • Move from simple scripts to a mature agent framework if:
    • You manage many tools or complex multi‑step plans.
    • You want built‑in tracing, retries, and evaluation tooling.
  • Introduce background jobs or queues if:
    • Tasks can take longer or involve multiple external systems.
    • You need reliability and back‑pressure handling.
  • Consider fine‑tuning or adapters if:
    • You repeatedly correct the same behavioral issues.
    • Prompt‑only iteration no longer delivers improvements.

Only add complexity when your manual process is well understood and the need is clear; otherwise, frameworks can obscure what’s actually going wrong.


Putting It All Together: Your Manual LLM Agent Workflow

Here is a concise checklist you can reuse for any new LLM agent:

  1. Pick a narrow, high‑value, low‑risk use case.
  2. Write the manual, human‑only workflow step by step.
  3. Select an appropriate model and hosting option.
  4. Draft a system prompt that encodes role, scope, behavior, and safety.
  5. Define tools with clear arguments, returns, and usage rules.
  6. Implement a simple orchestration loop for tool use and multi‑step reasoning.
  7. Enforce structured inputs and outputs where possible.
  8. Run manual tests on realistic, diverse scenarios.
  9. Add guardrails, logging, and human review for early production.
  10. Only then, consider frameworks, automation, and tuning to scale.

By treating your first LLM agent as a small, well‑defined workflow rather than a magic box, you dramatically increase your odds of shipping something reliable, safe, and genuinely useful.

Use this manual workflow as your template. Each new agent starts with these same fundamentals—only the domain, tools, and prompts change.