At its core, agentic AI is a system built around an AI model that enables it to operate more like an active teammate, capable of setting goals, taking action, and working independently. This system uses memory (to retain context), tools (like access to your codebase or terminal), a defined goal, and the autonomy to act. The model provides the reasoning; the surrounding system transforms that reasoning into purposeful execution, allowing the agent to plan, adapt, and complete tasks with minimal human input.

Today, teams are starting to explore agentic AI in scenarios such as repetitive business tasks and complex workflows with minimal oversight. This is especially true in software development where new agentic AI solutions are rapidly being embraced by engineers to accomplish key tasks much faster, and deliver value at scale. Agentic systems support people by taking on routine tasks, freeing them to focus on strategy and creative problem solving. This shift moves AI from a passive tool to an active partner in daily work.

How does agentic AI work?

Agentic AI follows a clear, repeatable process that turns instructions into real actions. Here’s how it works:

1. Receives a goal: Every AI agent starts with a goal given via natural-language prompts. This goal can be as simple as generating and testing code or as complex as managing a multi-step workflow. Clear goals help the agent decide what actions to take and how to measure progress.

2. Plans tasks: Once it understands the goal, the AI agent breaks it down into steps. For example, if the goal is to generate code, the agent will plan each step from writing the code to deployment.

3. Uses tools and context: To accomplish its goal, agentic AI connects to tools like a code repository, terminal, or scheduling system. It uses memory to keep track of what’s done and what needs to happen next. This context helps it work independently without losing track of the bigger picture.

4. Takes action: Unlike simple generative AI chatbots, which stop after creating content in response to a prompt, an AI agent carries out each step of the plan until the goal is verified complete. If something unexpected happens—like a failed test—the agent can adjust its plan in real time.

5. Learns and adapts: As it works, the agent monitors what’s working and what isn’t. This feedback loop lets it make better decisions the next time a similar task comes up. In software development, in particular, this allows agents to avoid hallucinations that sometimes affect generative AI models. The code either works or doesn’t work and is tested throughout the generative process.

Together, these steps let agentic AI handle work that once needed constant human input. By acting on goals, managing tasks, and learning from results, AI agents can help teams spend less time on repeatable steps and more time on solving problems that push innovation forward.

Agentic AI vs. generative AI

Agentic AI and generative AI share a foundation but serve different purposes. Generative AI focuses on producing new content based on patterns in data. It helps write text, create images, or suggest ideas by responding directly to prompts. Once it delivers a result, its job is done until the next prompt comes along.

Agentic AI builds on these generative abilities but has more independence to execute on goal-oriented action. An AI agent doesn’t stop after generating a single output in response to a prompt—it continues to iterate on its own outputs to carry out key tasks and learn from the results. While generative AI helps create, agentic AI helps act. This distinction shapes how each fits into daily work.

Benefits and challenges of agentic AI

Agentic AI introduces new ways to handle tasks that once needed constant human oversight. By combining generative capabilities with the ability to perform independent actions, agentic systems open up opportunities to simplify work and boost productivity—and that’s particularly true on software development teams.

Key benefits of agentic AI

Agentic AI brings several advantages that help software development teams work smarter:

• Increases efficiency and scalability: An AI agent can manage repetitive or time-consuming tasks with minimal supervision. By doing so, teams can scale processes that would otherwise demand more people or hours, freeing up capacity for higher-value work.

• Automates complex workflows autonomously: Unlike more simple automation tools, agentic AI adapts to changing inputs and conditions. An AI agent can coordinate multiple steps in a process, make decisions along the way, and complete workflows end to end. This makes it easier to automate tasks like code modernization, refactoring jobs, or securing vulnerabilities in a codebase.

• Responds quickly to changing conditions: Agentic AI doesn’t just follow static instructions. It observes what’s happening and makes updates in real time. This means an AI agent can adjust actions if priorities shift or new data appears, keeping tasks on track without constant human intervention.

• Frees up people to focus on high-value tasks: By handing off routine or multi-step tasks to AI agents, teams have more time to solve complex problems, test new ideas, and push innovation forward. This balance often leads to greater productivity and more space for creative thinking.

Agentic AI risks

While agentic AI has clear benefits, it also introduces new risks that businesses and software teams need to address. Like any AI-assisted tool, agentic systems depend on careful design and oversight to protect people, data, and outcomes.

Potential risks include:

• Prompt injection: If not secured properly, an attacker could secretly insert malicious prompts into an AI system. These hidden instructions can override or change what the AI agent was originally asked to do. Without strong safeguards, prompt injections might cause the agent to take unintended actions that disrupt workflows or expose systems to risk.

• Sensitive data leaks: Agentic AI often handles large amounts of information. Without the right controls, an AI agent could unintentionally share or reveal sensitive data. This risk grows as agents without proper guardrails connect with multiple systems and pull information from different sources.

• Supply chain vulnerabilities: Agentic AI’s ability to connect across systems brings efficiency but also creates new entry points for misuse. If supply chain connections aren’t well protected, attackers could target autonomous workflows to disrupt operations or gain unauthorized access to critical data.

• Bias: An autonomous AI agent that makes independent decisions can amplify bias if its training data or instructions aren’t monitored closely. Hidden bias may influence results in ways that create unfair outcomes or introduce compliance risks.

• Misalignment: When an AI agent’s goals or actions drift away from human intent, it can make decisions that don’t match business needs or ethical standards. This misalignment may lead to unintended outcomes that affect people or processes in unexpected ways.

• API misuse: Agentic AI often relies on multiple APIs to carry out tasks. Without strong controls, attackers or bad actors could misuse these connections to gain unauthorized access or disrupt operations.

Common challenges

Deploying agentic AI successfully means tackling security and privacy from the start. Failing to protect systems and data slows down adoption and can damage trust.

Beyond technical barriers, there’s also a human factor. Many leaders and employees may hesitate to trust an AI agent without clear proof that data is safe, decisions are fair, and oversight remains in place. Organizations that build trust and transparency into their agentic AI solutions are more likely to see strong adoption and better outcomes for everyone involved.

Use cases for agentic AI

Agentic AI is beginning to shape how different industries approach automation and decision-making. By blending generative abilities with autonomous actions, AI agents handle tasks that once depended entirely on people. Here are some ways agentic AI is creating value across industries today.

Software development: Streamlining coding and reviews

Agentic AI helps teams build software end to end. Teams use AI agents to plan projects, generate and refactor code, create documentation, monitor and optimize performance, and optimize continuous integration/continuous deployment pipelines.

Agentic AI also assists with AI code reviews. An AI agent can automatically check for bugs, flag potential security issues, and suggest fixes. This ongoing support speeds up development cycles and helps teams deliver more reliable software. With agentic AI, developers spend less time on routine edits and more time solving complex problems and building new features.

Healthcare: Supporting care and operations

In healthcare, agentic AI helps software development teams improve patient experiences and streamline behind-the-scenes work. For example, researchers are exploring AI-assisted agents that can handle administrative tasks such as scheduling follow-ups or transcribing and summarizing patient visits in real time.

Beyond the clinic, software teams use agentic AI to support supply management by automatically restocking essential medical supplies based on usage trends and inventory levels. They are also testing autonomous AI agents for patient monitoring, where the system checks real-time data and alerts caregivers when conditions change. These applications help staff focus on direct patient care instead of repetitive tasks.

Finance: Automating customer support and fraud detection

Financial services rely on quick, accurate responses to customer needs. Software development teams support this by using agentic AI to automate routine customer interactions. For example, banks are piloting AI agents that handle everyday requests like card activations, account updates, and payment reminders.

More advanced agentic systems can monitor transactions for unusual activity and take immediate actions, such as flagging accounts for review or locking cards if fraud is detected. By reacting in real time, these AI agents reduce manual effort and keep customers safer.

Manufacturing: Managing supply chains and maintenance

Manufacturers often face complex supply chain decisions that change daily. Development teams help keep production on track by using agentic AI to independently adjust orders or delivery schedules when delays or shortages arise. They also use agentic AI systems to coordinate between suppliers, warehouses, and logistics providers to reduce downtime.

On the shop floor, software teams deploy AI agents to watch for signs of equipment wear and plan maintenance automatically. For example, if a machine shows signs of overheating, an autonomous AI agent might schedule a service call and order parts before a failure occurs. This proactive approach minimizes disruptions and extends equipment life.

Future outlook of agentic AI

Agentic AI is moving fast, reshaping how we think about automation and decision-making. As software development teams look for ways to work smarter and adapt quickly, agentic AI is emerging as a key part of this shift. Here are a few ways agentic AI is blending autonomy with AI-assisted creativity to set the stage for more connected, self-optimizing workflows in the years ahead.

Rise of hyperautomation

Unlike simple task automation, hyperautomation goes beyond isolated bots or scripts. It connects tools, systems, and people so that repetitive tasks, decision points, and even complex workflows run with minimal human input.

Agentic AI is taking hyperautomation further by combining intelligent agents with robotic process automation (RPA) to foster end-to-end, autonomous task execution. These systems dynamically adapt to real-time conditions—replanning orders, rerouting logistics, or reallocating resources—with out waiting for human input.

Multi-agent ecosystems

Rather than relying on single agents, businesses are deploying networks of specialized AI agents that communicate, orchestrate tasks, and solve complex workflows together—such as inventory agents syncing with finance and HR counterparts.

Self-evolving architectures

Agentic AI is growing more adaptive—updating strategies, reallocating resources, and even rewriting parts of its workflows based on performance data.

Governance-first deployment

With greater independence comes greater responsibility. Leading organizations are embedding explainability, audit trails, and security protocols to maintain trust and meet regulatory obligations under frameworks like the EU AI Act.

Integration with IoT and cloud

Cloud platforms and IoT devices are being integrated into agentic workflows, allowing for real-time decision making at the edge such as factory sensors that trigger immediate maintenance tasks.

Agentic AI is poised to transform workflow optimization. As agents grow more intelligent, collaborative, and context aware, they’ll become central to operations by supporting self-correcting workflows, real-time event responses, and even marketplaces of specialized agents. That future moves beyond automation: it’s about truly autonomous, optimized processes that support strategic human work.

How to get started with agentic AI

Getting started with agentic AI begins with a clear plan. Development teams that succeed with agentic systems treat them as part of a broader strategy for smarter, more adaptive workflows—not just as a standalone tool.

Start with a focused strategy

First, identify where agentic AI adds the most value. Look for repeatable, time-consuming processes that need more than simple automation. These might include code generation and refactoring, continuous integration/continuous deployment optimization, or managing real-time data flows. Set clear goals for how an AI agent should assist your teams and build guardrails to keep actions aligned with company policies and ethical standards.

Adopt the right technology

Next, choose tools and platforms that make it easier to test, deploy, and scale agentic AI safely. Many organizations start small—piloting agentic workflows in one software team, learning how the AI agent performs, then expanding when they see results. Integrate security and compliance checks into every stage to protect data and keep sensitive systems safe.

Build the right skills and mindset

Agentic AI works best when people know how to work alongside it. Equip teams with skills in prompt design, workflow oversight, and monitoring so they can guide AI agents effectively. Encourage a culture where teams trust AI-assisted tools while staying involved in decisions that need human judgment.

Agentic AI at GitHub

GitHub Copilot is an example of agentic AI helping developers move from repetitive tasks to one-click actions. Copilot goes beyond code suggestions with AI agents that handle multi-step workflows like editing files, writing tests, and suggesting fixes across files. With Copilot agents, developers can offload manual tasks like editing, refactoring, and test generation. This creates more time for innovation, improves team focus, and drives consistent output across projects.