How to Build Agentic AI:
Key Steps, Cost & Challenges (Guide)

AI initiatives may fail in production because they can’t meet real-world requirements. That’s why mature leaders invest time in learning how to build agentic AI that interprets data correctly, applies all required business rules, and completes tasks without constant oversight.

Unlike chatbots or LLMs that only sound correct (at least that’s what developers focus on), AI agents must stay factually consistent and reason through every step.

This guide describes how to build agentic AI from scratch, given all the building blocks and development challenges that derail adoption.

Agentic AI is software that can complete tasks or produce responses with limited supervision. It consists of machine learning models that perceive tasks, interpret facts, plan completion, involve extra tools (or other agentic workflows), and reflect on the results.

Businesses build agentic AI from scratch because of the advantages of autonomy. Most other AI tools merely provide rule-based automation. However, agentic and AI agents can make choices, test different methods, and change the approach mid-task if something goes wrong.

For example, instead of only drafting an email, an AI agent can check CRM data, personalize the message, send it, and track replies.

Agentic AI operates in a loop that allows it to observe, decide what to do, and assess whether the outcome aligns with the goal. These building blocks work together sequentially because of the following core modules.

Each module turns AI into a semi-autonomous tool. But to make it all work, you need to build agentic AI systems with a technical stack that gives it memory, safety, and controls:

• A machine learning model (often a large language model) detects patterns, interprets inputs, and generates responses.
• Retrieval augmented generation (RAG) lets the system collect information from internal sources (such as documents, databases, or knowledge bases).
• Memory store is a data layer that keeps track of what the system has already done or learned during a task.
• Application programming interface (API) integration is necessary to build agentic AI that can use other software and databases.
• Control logic helps the system decide the order of steps, fallback paths, and success conditions.
• Safety and guardrails define what the AI agents and agentic workflows are allowed to access, blocking dangerous actions and preventing personally identifiable information (PII) leakage.

With these components, the AI can act autonomously across business tasks.

Agentic AI creates value by automating progress toward a result. Let’s break down why businesses adopt these systems:

• Automated operations across systems. Agents handle multi-step workflows without supervision and recover from missing or unclear data by retrieving extra details or trying alternative paths.
• Increased value from existing tools. Building agentic AI applications helps companies bridge existing tools and databases that they couldn’t use in real time before due to disconnected systems.
• Reduced operational risk. The agent checks conditions and rules before acting, which prevents it from violating company policies and regulations.
• Enabled workflows during off-hours. An agent can monitor, intervene, and resolve customer issues when human operators are unavailable.
• Consistent excellence without increasing headcount. An agentic system makes it possible to operate continuously and scale according to your business needs without losing focus or reducing output quality.
• Shortened time to value. A business can build an agentic AI with a narrow workflow, measure the impact, and expand gradually, which helps validate the return on investment (ROI) before scaling.

Agentic AI is effective when it’s built deliberately, starting from limited and defined tasks and expanding only after it proves it operates safely.

Most failures come from teams rushing and experimenting without a clear goal. Instead, you should be building an agentic AI application with a problem-first approach. The workflow itself becomes the blueprint, and the agentic system is shaped around it.

1. Identify a Real Need or Problem

Before building agentic AI, you should define what it’s supposed to accomplish in practical terms. Avoid vague objectives that you can’t measure or audit.

Focus on a real business process you want to enhance with automation. Name the task the agentic AI will own, the data it must read, the tools it will use, and what qualifies as a finished outcome.

2. Assess the Organizational Readiness

Audit your company’s infrastructure and applications to make sure it has the technical footing required to run it safely. Agentic systems require compute resources for reasoning, a storage layer for memory, and access to your other applications and tools.

3. Select a Workflow to Automate

Narrow the scope of your agentic AI development project. Ideally, you should target a single workflow that occurs often, follows clear rules, and creates cost or delay when handled manually. A good rule is to choose a process that a trained human could complete using clear rules and success conditions.

4. Choose the Architecture and Model

The architecture for the agentic AI defines how it will treat working memory and long-term memory, which impact the stability and repeatability of its decisions. You should configure what it will perceive as temporary working memory, what is stored as past executions, and what is treated as permanent reference knowledge.

You can combine several models to build your own agentic AI. Some workflows require fixed branching logic, while others need a planner that selects the next step based on conditions. For example, your process could use a smaller model for data routing and policy checks (to prevent unsafe actions), and a larger model for complex reasoning.

5. Enforce Rules and Permissions

AI can escalate the wrong decision or repeat a harmful action until it causes damage. That’s why you should create explicit boundaries for the actions the AI is permitted to take without supervision. This includes the data it may read, systems it can write to, and actions that always require a human checkpoint.

It’s critical that you know how to build an agentic AI system with preventive controls from the get-go. If they are added after deployment, the agent may already have access to more than it should.

6. Confirm Test Cases

Test cases specify what correct behavior looks like under real conditions. Try to make each one cover the full workflow, not just a small slice.

They are built from live historical data with common processes, such as past support tickets, onboarding submissions, workflow logs, and transaction records. But the test coverage should include edge cases (unusual scenarios) that validate whether the agent will pick the correct fallback path or request a human review instead of forcing through a bad decision.

An important consideration is to ensure that the test data doesn’t have any personally identifiable information (PII). Anonymize the original records or replace them with synthetic data that preserves the structure of the original datasets without exposing sensitive data.

7. Train and Refine the Model

Most of this training happens through prompt design, retrieval setup, and reinforcement from evaluation runs. At this stage, you should teach the agentic AI how to interpret inputs, pick the right records to retrieve, and choose the correct actions that follow your business policies.

Building agentic AI systems means constantly refining. The model is tested repeatedly on the golden set of examples, proving it can reach the correct outcomes efficiently. If the system is correct but uses costly action paths, you should keep optimizing before moving forward.

8. Validate the Agent

Create a minimum viable product (MVP) of the agentic system before expanding the scope. This is basically a stripped-down version of the agent that handles only the core steps of the workflow, so you can validate that the logic, permissions, and expected outcomes are correct.

Early validation gives you early feedback that lets you understand how to build an agentic AI further: what to prioritize, discard, or redo. It also helps you realize if the project is worth finishing. Early prototypes let you collect feedback from internal users and stakeholders, which reveals hidden assumptions and unclear logic that the development team might miss.

9. Connect the AI Agent with Your Stack

At this stage of developing agentic AI, you should define which endpoints it is permitted to communicate with and what data it can read or modify. Modern cloud-based tools are usually connected via APIs. Meanwhile, legacy systems may require you to create middleware and automation tools that will translate the agent’s requests into an understandable format.

The connector logic must handle paging through large datasets, retrying failed requests, and returning controlled errors if a downstream service cannot respond. Mind you, writing is only complete with a proper rollback. If the system writes half of a change and fails, it must undo the write instead of leaving the record in a broken state.

From finance and healthcare to retail and logistics, these are real use cases of agentic AI for businesses.

Finance

Companies use agentic AI to verify identity, check policies, process payments, and manage invoicing.

Healthcare

Healthcare organizations implement agents to analyze medical notes and media, update patient health records, and handle daily patient questions.

Insurance

Insurers implement agentic workflows that can perform underwriting, preliminary damage assessment, and basic claims processing.

Retail

E-commerce and retail platforms rely on agent-based virtual assistants, pricing strategy optimization, as well as personalized product recommendations.

Logistics

Transportation and logistics companies are building agentic AI that predicts demand, manages warehousing operations, and optimizes delivery routes.

Obstacles can arise at any stage, even after launch, so teams must plan for them early.

It’s easier to build your agentic AI applications with the right tech stack. Below are some of the tools that can work best for you, divided into categories based on their role.

Agent orchestration

Orchestration tools help control how agentic AI handles tasks, preserves state across runs, and coordinates teams of agents.

• LangChain and LangGraph assist with prototyping using plug-and-play components for memory, tools, and retrieval.
• Botpress is designed for creating agentic AI with a visual builder with almost no coding.
• OpenAI Swarm is a coordination layer for small and multi-agent systems.
• Microsoft’s Semantic Kernel and Agent Framework are suitable for enterprises that already use the Microsoft stack and want single-agent or multi-agent systems.

Cloud and model access

These platforms establish a layer that hosts the models, provides compute power, and controls operating costs.

• Major clouds like AWS, Azure, and Google Cloud host models and provide compute and storage.
• OpenAI offers hosted models that support enterprise security controls.
• Hugging Face is a web-based platform that helps fine-tune or self-host open AI models.

Vector databases

Databases store embeddings (numerical text fingerprints) that enable agentic systems to remember relevant information and past interactions.

• Pinecone is a managed vector search that works for large and performance-critical workloads.
• Weaviate appeals to teams that want open source and hybrid search.

Reinforcement learning

These tools permit agentic AI to learn from success and failure via a reward system.

• Stable Baselines3 provides ready-made reinforcement learning algorithms.
• PyTorch works better for a team that knows how to develop agentic AI with a custom model with together controls.

Observability

Observability tools let you observe how agentic AI reaches conclusions, the rules it follows, actions it takes, and whether the result was correct.

• LangSmith can replay the decision steps the model took to accomplish a goal.
• promptfoo runs test cases repeatedly to assess the model’s repeatability.

Companies are shifting toward agentic AI fast despite technological limitations. Gartner forecasts that 15% of daily business decisions will be made by autonomous agents by 2028 (up from zero in 2024). As adoption shifts from pilots to production, we are seeing some trends that are shaping their use.

• Success depends on the right use cases: Over 40% of agentic projects will be canceled by the close of 2027 because they were built on hype instead of a clear business outcome. To avoid this, it pays to learn how to build agentic AI workflows on narrow processes and validate success with measurable performance metrics before expanding.
• Customer service focus: According to Gartner’s 2025 report, agentic AI could handle 80% of customer requests by 2029. Most repetitive requests already follow clear resolution steps (like checking an account, applying a rule, invoking a tool, and updating a record), which are ideal for agents to handle without humans.
• Processing at the edge: Small models able to run locally on IoT devices (factory equipment, sensors, etc.) help reduce latency and keep the workflow running despite bandwidth limitations or connectivity issues.
• Compliance at the design level: Industries like healthcare or finance won’t invest in agents if there’s a chance they’ll break data privacy laws. To comply with the EU AI Act and similar regulations, these sectors will focus on building agentic AI systems with built-in guardrails, data minimization tools, and documented oversight.
• Modularity for agentic AI development: Instead of building a new stack for every agent, mature companies will create a shared infrastructure with a common foundation (like memory, retrieval, evaluation, and tool connectors) that makes it easier to plug new agentic AI into.
• Focus on verification speed: Firms will build agentic AI that can provide short explanations. Reviewers will see what data the agent used and which rule it applied, then approve or correct with one click.
• Cost governance as standard practice: Token and compute spend will be tracked heavily. More organizations will implement telemetry, spending caps, reviewer checkpoints, and other guardrails to monitor and control the costs of agentic workflows.

True autonomy requires reliability and safety. Systems that can’t explain or undo their actions aren’t ready for production. And, if you feel unsure about your team’s skills, it’s a safer bet to get agentic AI development services from an experienced company.

Do you have concerns about whether your team is experienced enough to develop your agentic system? DevCom helps teams build AI that survives contact with real environments. Reach out for guidance on building or deploying your first production-grade agentic system.