As LLMs advance, modern AI-powered development platforms are moving beyond basic AI-assisted autocomplete toward systems that can plan and execute complex tasks. These agentic AI systems are serving more of a collaborator role with developers, enabling them to build features that once took weeks or months in seconds.
As the term suggests, at the core of agentic coding are coding agents—AI systems that combine reasoning capabilities from LLMs with access to coding tools and execution environments. Unlike simple chat interfaces, coding agents operate across multiple layers of the development stack.
Early AI coding tools focused primarily on productivity enhancements, such as autocomplete and inline suggestions within an IDE or development environment. These tools analyzed existing syntax and nearby code to suggest the next line of code. Modern systems go further.
Platforms powered by large models, such as those developed by OpenAI, Anthropic, Gemini and IBM, can interpret natural language requests, reason about entire repositories and generate structured tasks for execution. This advancement enables agentic AI systems to orchestrate complex development processes. For example, a real-world developer might request the following from a coding agent:
“Add authentication to the front end, update the API, and include unit tests.”
The automation of this sequence of tasks is made possible with the use of an AI agent. An agent can also use other specialized subagents that focus on particular tasks. By coordinating multiple agents, these systems can quickly work toward higher code quality.
Get curated insights on the most important—and intriguing—AI news. Subscribe to our weekly Think newsletter. See the IBM Privacy Statement.
The surge of terms like agentic coding, vibe coding and agentic engineering reflects a broader shift in how developers interact with AI. However, it is easy to confuse the several terms for AI-assisted coding, especially as new terms are coined each year. To help distinguish some of the most frequently used terms, we can think of them on a spectrum.
Vibe coding sits at the informal end of the spectrum as it is driven by natural language prompts, often prioritizing speed and exploration over structure or rigor. In contrast, agentic coding introduces more discipline; developers orchestrate AI agents with defined roles and constraints to produce code that can be tested and iterated on.
Agentic engineering goes a step further, treating these systems as production-grade software. This more technical end of the spectrum emphasizes system design, reliability, observability and collaboration between multiple agents and humans. In practice, these approaches aren’t mutually exclusive. Teams often start with vibe coding to prototype ideas quickly, then evolve toward agentic coding and ultimately agentic engineering as the stakes and complexity increase.
Generating code is one of the most popular use cases for AI agents. In agentic workflows, coding agents can:
Not only can they write code, but they can also improve the code quality and optimize the software development cycle. These capabilities allow us as humans to focus on tasks that require nuance and deep expertise.
There are numerous ways in which agentic coding allows software engineers to be more productive and use their time more meaningfully. Developers can use AI tools to handle the mechanical parts of coding that once consumed hours of focused effort like scaffolding a Python module or generating markdown documentation. This frees engineers to concentrate on the problems that require human judgment, like system design and creative problem solving.
Tools like GitHub Copilot and ChatGPT have already demonstrated how conversational AI can accelerate development at the function level. Agentic systems go further, leveraging an expanded context window to reason across entire codebases. Open source models and frameworks have also lowered the barrier to entry, allowing teams of all sizes to experiment with and deploy agentic workflows without significant infrastructure investment.
However, there are some considerations to have when working with AI-generated code. AI tools can produce output that looks correct but contains subtle bugs or security vulnerabilities, so every generated output should be treated as a first draft that requires careful review.
Without proper guardrails in place, teams risk introducing regressions or exposing sensitive systems to unintended behavior. There is also the risk of over-reliance: developers who offload too much of their reasoning to AI tools might find their foundational skills quietly eroding over time.
Getting the most out of agentic coding involves some key principles.
Establish what agents are and aren’t permitted to do in your environment. Limit write access, enforce testing requirements before merging and log agent actions.
Act as the human-in-the-loop to verify agent output, especially for consequential tasks such as merging to production and modifying infrastructure.
Log agent decisions, tool calls and intermediate outputs. This visibility is essential for debugging.
AI agents are powerful, but they are not mind readers. Define clear success criteria and supply up-to-date code and architecture docs to reduce hallucinations.
Treat agentic coding tools as collaborators. Provide your feedback to refine and correct agent output.
Like any powerful tool, the value that agentic coding delivers depends on how thoughtfully it’s used. Teams that invest in good processes will be best positioned to benefit as the technology keeps improving. That means reviewing AI-generated code with the same rigor one would apply to any pull request, keeping humans in the loop on consequential decisions and resisting substituting automation for understanding.
Accelerate software delivery with Bob, your AI partner for secure, intent-aware development.
Optimize software development efforts with trusted AI-driven tools that minimize time spent on writing code, debugging, code refactoring or code completion and make more room for innovation.
Reinvent critical workflows and operations by adding AI to maximize experiences, real-time decision-making and business value.