How to develop an LLM agent (Without building one from scratch)

Davis Christenhuis

-April 2, 2026

Developing an LLM agent has become more accessible as platforms and frameworks mature. The technical complexity ranges from choosing which model to use to designing how agents handle multi-step tasks.

This guide covers how LLM agents work, what building one from scratch requires, and how agent platforms offer an alternative to custom development.

📌 TL;DR

Pressed for time? Here's what this guide covers:

What LLM agents are: AI systems that reason, plan, use tools, and execute multi-step workflows autonomously using a large language model as their intelligence layer.
How they work: Four core components work together: agent core for reasoning, memory for context, planning for task breakdown, and tools for taking action.
What building requires: Selecting models, designing orchestration logic, building memory systems, connecting data sources, and ongoing maintenance.
Using a platform: Dust lets teams build agents by writing instructions, connecting data sources, and choosing models, with no code required for most use cases.

What is an LLM agent?

An LLM agent is an AI system that uses a large language model as its reasoning engine to break down complex tasks, decide which actions to take, access external tools and data sources, and execute multi-step workflows autonomously. The choice of language model affects how the agent reasons, how well it follows complex instructions, and how it handles unclear tasks.

LLM agents work toward defined goals by planning sequences of actions, retrieving information from connected systems, and adapting their approach based on what they discover at each step. The agent reasons about what to do next, executes that action using available tools, evaluates the result, and continues until the task is complete.

That autonomy separates agents from other AI applications. They don't just process inputs and generate outputs. They orchestrate work across multiple systems without requiring human intervention at every step.

💡 Want to see how agents work in practice? Discover Dust →

How LLM agents actually work

Most LLM agents share four core components that work together:

Agent core: The LLM brain that processes inputs and reasons through tasks. It interprets requests, determines which actions to take, and generates responses based on the information it gathers. The model you choose affects reasoning capability, speed, and cost.
Memory: Short-term memory holds the current conversation context within the model's context window. Long-term memory stores past interactions, user preferences, and retrieved knowledge that the agent can reference across sessions. Company-specific knowledge is typically handled through connected data sources and retrieval systems rather than memory alone. When configured with persistent memory, agents can maintain context across sessions and reference information from previous interactions with a given user.
Planning: How the agent breaks down complex tasks and decides what to do next. Planning can happen upfront with all steps defined at the start, or dynamically where the agent adjusts based on what it learns at each stage. ReAct (Reasoning + Acting) interleaves verbal reasoning traces with task-specific actions, where reasoning informs which actions to take and observations update the reasoning at each step. Plan-and-Execute approaches generate an upfront plan, execute each step, then re-evaluate and replan based on what was learned.
Tools: What the agent can act on beyond conversation. Search APIs let agents find information. Code interpreters execute calculations and generate charts. Database connections enable querying structured data. CRM integrations allow record updates. Function calling lets LLMs decide when to use a tool and structure the required input parameters, though implementations vary across providers. Model Context Protocol (MCP) standardizes how tools and data sources are exposed and discovered across AI applications, creating a shared infrastructure for tool connectivity.

Understanding these components is important because developing an LLM agent means building or configuring each of these layers to work reliably together.

What does developing an LLM agent actually require?

Building an LLM agent from scratch involves layers of complexity that development timelines often reveal:

Selecting and managing LLM providers: Development requires choosing between providers like OpenAI, Anthropic, Google, or open-source models, each with different rate limits, cost structures, and API behaviors. Supporting multiple models means managing vendor relationships and handling API changes as they roll out.
Designing the agent loop and orchestration logic: The agent needs a control flow that determines when to plan, when to act, and when to stop. This includes retry logic for failed actions, timeout handling, error recovery, and guardrails that prevent runaway loops.
Building memory and context management from scratch: Short-term memory requires managing context windows and deciding what to keep or discard as conversations extend. Long-term memory typically involves vector databases for semantic search, storage systems for past interactions, and retrieval mechanisms that surface relevant information when the agent needs it.
Connecting and maintaining data source integrations: Business systems need connectors that handle authentication, normalize data formats, and respect permission boundaries. Third-party API changes can break integrations, requiring ongoing maintenance to keep agents working reliably.
Evaluation, testing, and ongoing debugging: How do you know if your agent works? Testing requires defining success metrics, building test suites that cover edge cases, monitoring production behavior, and implementing observability so you can debug when agents fail.
Engineering resources and timeline: Building a functional agent requires development time and engineering work. Production systems need additional investment and ongoing maintenance as models and integrations evolve.

This investment makes sense for companies building AI products or handling requirements that standard platforms cannot address. Agent development platforms offer an alternative by handling infrastructure and maintenance for teams that prefer not to build from scratch.

Build vs. configure: two paths to developing an LLM agent

There are two ways to develop an LLM agent. Which one fits depends on your team's resources and timeline.

	Build from scratch	Use a platform
Time to deployment	Longer development cycles	Faster implementation
Engineering required	Yes (dedicated team)	No or limited
Model flexibility	Full control over architecture	Platform-provided model options
Data integrations	Build and maintain manually	Platform-provided connectors
Maintenance burden	High (API changes, updates)	Handled by platform vendor
Cost structure	Development time + infrastructure	Platform subscription
Best for	Teams needing full custom control	Teams prioritizing speed and reduced complexity

Building from scratch gives you complete control over architecture and behavior. This path fits teams building AI products, exploring new research approaches, or handling highly specialized requirements that require custom infrastructure.

Using a platform trades some control for speed and reduced complexity. Platforms handle infrastructure, security, and integrations while you define agent behavior and connect your data.

How to develop an LLM agent with Dust

Dust is a platform that lets teams build and deploy AI agents without code. The platform connects agents to company data across Slack, Notion, Google Drive, Confluence, GitHub, and many other systems.

All while handling model orchestration, security, and infrastructure. The platform is GDPR compliant and SOC2 Type II certified, enables HIPAA compliance, and ensures your data is never used for model training.

Dust is also model-agnostic, meaning you can choose different LLMs for different agents based on what each task requires: OpenAI, Anthropic, Google, Mistral, and more. You match the model to the work.

Building an agent in Dust follows a few steps:

Step 1: Set up your agent — Choose your model from OpenAI, Anthropic, Google, Mistral, or others based on the reasoning depth and speed your use case requires. Create your agent and name it clearly to indicate its function.
Step 2: Write your instructions — Define your agent's role, task, and behavior in plain language. Specify what it should do, what data it needs to reference, and any constraints on how it should operate. Instructions control agent behavior without code.
Step 3: Connect your data and tools — Link the data sources your agent needs: Notion for documentation, Slack for conversation history, Google Drive for files. Add capabilities like web search to find external information, SQL queries for structured data, HubSpot for CRM data, or actions through connected tools.
Step 4: Test and deploy — Preview how the agent responds in the builder interface. Check which data sources it queries and what information it retrieves. Iterate on instructions based on actual output. When it works reliably, share it with your team.

In this example, the agent builder shows how simple setup works. On the left, you write clear instructions in plain language and connect data sources. In this case, Company Data with web search for external information and Notion and Slack for internal knowledge.

On the right, the preview shows a live response. When asked "How many integrations does Dust have?", the agent searches connected sources, retrieves the answer (52 integrations), cites where it found the information, and structures the response with a categorized breakdown.

The entire process took 35 seconds from question to answer.

Want the full step-by-step walkthrough? → How To Build An AI agent (2026)

💡 Ready to build your first agent? Try Dust free →

Use case: How Alan's PMM team deployed agents with Dust

Alan is a digital health partner operating across France, Spain, Belgium, and Canada, supporting over 720,000 members. Founded in 2016, the company combines technology with healthcare to make health journeys more accessible.

Alan's Product Marketing team needed to monitor how sales reps delivered key narratives across many of discovery calls monthly in multiple countries. Three product marketers spent 2-3 hours each per week manually reviewing calls, but they could only analyze a small sample. Most conversations went untracked, leaving narrative adoption insights incomplete.

The team deployed country-specific agents to automate their call analysis workflow:

Automated data retrieval: The agents pull call transcripts directly from Modjo, Alan's conversation intelligence platform
Individual analysis: Each transcript is processed separately to ensure accuracy and avoid errors
Country-specific evaluation: Separate agents for each market account for local narrative differences and messaging priorities
Narrative scoring: Agents score every call against Alan's five-block narrative framework
Structured reporting: The system outputs weekly reports with actionable insights for sales leaders

The results: The team went from analyzing a sample to reviewing 100% of discovery calls while eliminating manual analysis time entirely. Alan reclaimed 80% of their analysis time while delivering 10x the insights.

The product marketing team shifted from reactive analysts to strategic advisors identifying narrative gaps and coaching opportunities across the sales organization.

💡 See how other teams use Dust agents. Read customer stories →

Frequently asked questions (FAQs)

What is the difference between an LLM and an LLM agent?

An LLM processes text and generates responses based on its training data. It answers questions, summarizes content, or drafts text based on prompts. An LLM agent uses an LLM as its reasoning engine but adds planning, memory, and tool use to complete multi-step tasks. The LLM provides intelligence while the agent architecture provides execution capability. For example, an LLM can explain how to update a CRM record. An LLM agent can search your CRM for relevant records, update the fields based on new information, and confirm the change was successful.

Do I need to know how to code to develop an LLM agent?

Not necessarily. Building an LLM agent from scratch requires programming skills to design the agent loop, manage memory systems, and integrate tools. Configuring an agent on a platform like Dust requires no code. You write instructions in natural language, connect data sources through a visual interface, and test in a chat window. Business teams without developers can deploy functional agents this way. Coding becomes necessary when building custom integrations, or when developing agents using frameworks like LangChain or LlamaIndex. Building entirely from first principles, using raw model APIs without any framework, requires the deepest level of engineering investment.

Do LLM agents learn from their mistakes?

Not automatically. The underlying language model's weights are fixed unless the provider releases an update. However, agents can improve their effectiveness over time through persistent memory that stores user preferences and past corrections, growing knowledge bases they retrieve from, and feedback loops where human input refines instructions and guardrails.

Can LLM agents work in different languages?

Yes. Most modern LLMs support multiple languages out of the box, including English, Spanish, French, German, Chinese, Japanese, and many others. The quality varies by language and model, with leading models performing well across major global languages while performance may drop for less common languages or specialized terminology. Agents can respond in a single language or handle multilingual conversations where users ask questions in their preferred language.

AI agents for enterprise: What they do and how companies deploy them — Explains what makes agents enterprise-ready, including security requirements, permission controls, compliance frameworks, and real deployment examples.
AI Agent vs LLM: What is the best fit for you? — Breaks down the core difference between LLMs that generate text and AI agents that execute workflows, with a decision framework for when to use each.
Build vs. buy AI agents: What 1,000+ enterprise deployments taught us about the real costs — Covers the hidden costs of building LLM agents from scratch, realistic timelines, and why most companies choose platforms over custom development.