The future of software development? Agentic coding (and it’s vibey)

This shift is more than hype. Tools like Cursor, Claude Code, and GitHub Copilot Agent are proving their worth in real world workflows empowering developers to generate entire modules, refactor legacy systems or scaffold new applications - all with a prompt. These agents behave like tireless junior engineers capable of generating, testing and even debugging code at astonishing speed. And as the tools mature, the conversation is less about whether this will change development and more about how fast.

What is vibe coding and how is it connected?

“Vibe coding” might sound like developer slang but it’s become the poster term for this AI-driven way of working. Coined by AI researcher Andrej Karpathy, vibe coding describes the process of telling the AI what you want, letting it generate something, running it, debugging as required and refining the result - often without much upfront planning.

While agentic coding is the technical term, vibe coding captures the feel: expressive, fast, and iterative. You “see stuff, say stuff, run stuff.” The distinction? Vibe coding leans into creative exploration. Agentic coding brings discipline. In high-stakes domains like fintech or insuretech, that discipline matters. Treating the AI as a junior developer with clear guidance, code reviews and structured rules keeps things on track. Think of vibe coding as the fun cousin; agentic coding is the one you trust to ship a critical feature.

The role of MCP in agentic coding

As powerful as AI agents are they have limits, especially when it comes to understanding large codebases or pulling in external context. That’s where the Model Context Protocol (MCP) comes in.

MCP is a new open standard created by Anthropic that allows AI models to fetch information from outside their current context - code, documentation, databases and even additional prompts. Rather than rely solely on what’s typed into a chat, an MCP enabled agent can query your repo for architectural diagrams, query Jira for bugs or pull customer data from a CRM system while figuring out how to solve a problem.

In the world of agentic coding, MCP is a game changer. It makes AI agents smarter, more context-aware and vastly more useful. Instead of hitting the ceiling of a model’s context window you get a dynamic assistant that can reach across your tech stack to generate better, safer and more tailored code grounded in your domain.

Teams are embracing (and cautious of) agentic coding

Like any new technology, agentic coding brings a mix of opportunity and challenge. The upside is significant:

• Speed to market: Prototypes that once took weeks can be built in hours.

• Developer efficiency: Repetitive or boilerplate tasks are offloaded to agents freeing up humans for strategic thinking.

• Legacy modernization: Coding agents can help unravel old codebases, suggest upgrades or even translate between languages or frameworks.

• Accessibility: Non-developers or domain experts can contribute more directly, reducing bottlenecks in cross-functional teams.

But it’s not all smooth sailing. There are valid concerns:

• Code quality: AI is good but not perfect. It can produce code that may look correct but hide subtle bugs.

• Security and compliance: Coding agents might not understand your business risks, security posture or regulatory requirements unless told.

• Maintainability: Code written by an agent may work, but can your team support it six months from now?

• Team readiness: Adopting agentic tools isn’t plug-and-play. It requires training, process updates and sometimes a cultural reset. 

• Cost: Tools like Claude Code are excellent but a developer could easily consume several hundred dollars in API credits in a day if overusing.

Trends shaping the future of agentic coding

Agentic coding is evolving fast and several trends are pushing it further into the mainstream:

• Multi-agent systems: Future setups may include specialized AI agents (planner, coder, tester) working together mirroring how human teams collaborate.

• Agent-to-agent frameworks: Protocols are emerging to that enable agents talk to each other seamlessly, automating multi-step workflows.

• Deeper integration via MCP: Expect tools to connect to everything from CI/CD pipelines to monitoring systems so the AI can not only write code but deploy, test and fix it too.

• Domain specific agents: Imagine an coding agent fine-tuned for insurance regulations or financial data formats. These are coming.

• Improved verification: New models and workflows will make AI better at checking its own work reducing the burden on human QA.

The long-term vision? Coding agents that can understand your business context, coordinate like a team and deliver robust software with minimal oversight. We’re not there yet but we’re getting closer by the month.

Final thought

Agentic coding is more than a trend, it is a shift in how software is imagined and built. For technology leaders, the question isn’t whether to adopt these tools but how to integrate them effectively and responsibly.

The smartest teams are using agentic AI not to replace developers but to empower them by freeing up time, amplifying creativity and building faster without cutting corners. The potential is massive. And the organisations that learn to harness it today will be the ones outpacing competitors tomorrow.