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Forbes
2 days ago
- Business
- Forbes
AI Coding Agents: Driving The Next Evolution In Software Development
Vikas Mendhe is a solution architect and digital transformation expert specializing in API-driven solutions in financial technology. As artificial intelligence continues to reshape industries, one of the most significant innovations in the software world is the rise of coding agents. They are reshaping how code is written, tested and maintained, marking a new era in software development. What Are Coding Agents? Coding agents are intelligent systems powered by large language models that write, debug and optimize code. They generate APIs, refactor legacy systems, write tests and even build apps with minimal input. Popular tools include GitHub Copilot, Amazon CodeWhisperer and Tabnine. New-generation assistants such as Cursor, Windsurf (recently acquired by OpenAI) and Cline focus on deeper IDE integration, context retention and developer autonomy. Industry Adoption The adoption of coding agents is gaining momentum across sectors. Tech companies are embedding coding assistants into their workflows, startups are exploring autonomous agents like AutoGPT and Devin for rapid prototyping and governments are integrating them cautiously for tasks like data transformation, compliance automation and internal tool development. While accuracy and oversight concerns remain, the shift toward AI-assisted development is well underway. Language-Specific Strengths Of Popular Coding Agents As coding agents continue to evolve, developers often look for tools that best support the languages they work in. • GitHub Copilot thrives in Python, JavaScript and TypeScript, with robust IDE integration. • Amazon CodeWhisperer specializes in Java, Python and JavaScript, featuring AWS-native tools and cloud focus. • Cursor excels in TypeScript and Python, with built-in memory and pair programming. • Tabnine supports Java, Python, C++ and Go with offline capability and customization. • Claude Code optimizes Shell, Python and Bash for terminal-based tasks. • Devin, a Python-based agent, enables complex, multi-step, end-to-end coding automation. Real-World Case Studies Let's just take a look at GitHub Copilot's applications in the real world. ANZ Bank's 2024 trial of GitHub Copilot showed engineers completing tasks 42% faster with improved code quality. Accenture's enterprise study found Copilot users coding 55% faster, with 90% reporting higher fulfillment. And a 2025 ZoomInfo case study involving over 400 developers reported a 33% code acceptance rate and 72% satisfaction. These findings show coding agents reduce repetitive work and free developers for higher-value tasks. Impact On Software Development Coding agents could transform software development from end to end. For developers, they act as smart copilots, automating repetitive tasks and simplifying complex workflows. Businesses gain faster delivery, lower costs and greater agility, turning ideas into prototypes in days instead of weeks. These tools also democratize development: Non-coders can build apps using natural language, and junior developers can produce better code with minimal oversight. Educational studies confirm this potential. AI code completion tools enhance student productivity and engagement while preserving problem-solving and conceptual learning. Programs such as the Stanford Institute for Human-Centered AI are exploring how such tools support computer science education at scale. Behind The Scenes Of Coding Agents Most coding agents are built on transformer-based LLMs such as OpenAI's Codex and GPT-4. Popular tools like GitHub Copilot and Amazon CodeWhisperer operate through IDE plugins, sending prompts to remote model APIs. GPT-4o mini supports a 128K token context window, enabling broader file-level reasoning. Claude 3.7 Sonnet offers 200K tokens for extended reasoning workflows. Gemini 1.5 Pro surpasses both with a 2M token context, ideal for workflows spanning entire codebases. More autonomous agents, such as AutoGPT and Devin, use frameworks like LangChain to chain prompts, memory and shell commands, completing multi-step engineering tasks with minimal human input. Terminal-Based Coding Agents In parallel, new terminal-based coding agents are emerging to support command-line workflows for professional developers. Tools like Claude Code, Codex CLI and Gemini CLI bring AI-powered development directly into the terminal environment, enabling agents to execute commands, write scripts and interact with live file systems, all while preserving developer autonomy. Coding Agents As A Service Despite advances, coding agents can still produce insecure or low-quality code. Safeguards like validation mechanisms and inline linting help, but human oversight remains essential. Rigorous testing, linting and code reviews should be part of every deployment pipeline. Code Quality, Security And The Role Of Supervision Despite advances, coding agents still generate insecure code and lack deep understanding of intent. Recent advancements have introduced better safeguards, validation mechanisms and inline linting. However, ongoing oversight remains essential. This underscores the need for rigorous testing, linting and human code review pipelines before production deployment. Getting Started With AI Coding Agents Before adopting AI coding agents, focus on clear, high-value use cases and choose tools suited to those needs instead of automating everything. Keep humans in the loop by ensuring AI-generated code undergoes rigorous testing, security scans and peer reviews. Research shows nearly half of developers don't fully trust AI output and often spend extra time debugging it. Be mindful of data privacy, intellectual property and licensing rules to avoid compliance issues, and set governance policies to prevent security blind spots and vendor lock-in. To mitigate common pitfalls—such as inaccurate code, scope creep, security risks and hidden costs—start with structured pilot programs that have measurable outcomes. Enterprise case studies show that successful rollouts often begin with controlled experiments, formal risk assessments and well-defined change management plans. Strong guardrails, clear policies and an ongoing review process help organizations capture productivity gains while maintaining quality and security. Conclusion Coding agents are not meant to replace human developers—they are tools that help make their work faster and easier. As more companies start using them, it's important to find the right mix between automation and human control. When used responsibly, coding agents can help teams work more efficiently, come up with new ideas and change the way software is built in the AI era. Everyone, not just developers, should understand what coding agents can and can't do, especially those shaping the future. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Time Business News
13-06-2025
- Business
- Time Business News
Beyond Automation: AI's Game-Changing Impact on App Development in 2025 ?
Earlier app development was linear. You had your specs, wireframes and sprints. Now AI is not just helping it's shaping how apps are planned, coded, deployed and improved. If you're a startup founder chances are you've either already tried integrating AI tools or you're considering it and it's about measurable gains in speed, aim and personalization. User behavior is now predictable. Platforms like Uizard and Galileo AI can take text prompts and generate wireframes or high-fidelity screens in seconds. AI also runs heatmap simulations, predicts user drop-off points and layout changes that improve user flow. Instead of endless A/B testing cycles, it offers data-backed insights in real time. GitHub Copilot, Amazon CodeWhisperer and Tabnine are making developers faster. AI doesn't just autocomplete lines of code, it understands context, syntax and logic structures. In 2025, many teams are building their own AI copilots trained on proprietary codebases, affirming AI understands company specific naming conventions and architecture. AI-powered tools like Diffblue and Testim generate test cases, detect anomalies and propose fixes before a developer notices a problem. This shift makes pipelines more efficient and reduces patches post-launch. GPT-powered assistants are being integrated in apps for customer support, onboarding and personal coaching. Beyond chatbots we're talking natural, conversational UX. AI engines personalize everything from content to push notifications. What used to take a data science team now happens using tools like Amazon Personalize or custom GPT based integration. AI enables continuous iteration in mobile app development. It collects real-time usage data and suggests improvements, UI changes, copy tweaks, feature prioritization. Apps are no longer delivered and forgotten, they develop daily. This approach shortens the build measure learning cycle, helping startups reach product market fit faster. While AI is changing the game, Some hurdles still include: Training data quality: Bad data means bad predictions. Bad data means bad predictions. Over reliance on suggestions: Junior devs may accept AI code without understanding it. Junior devs may accept AI code without understanding it. Security & Compliance: AI models handling sensitive data should meet high regulatory standards. AI models handling sensitive data should meet high regulatory standards. Tool overload: Not every AI tool is worth using, choosing wisely is key. The Actual value lies in knowing where to use AI and where to depend on human judgment. Is this improving development velocity? Do we have clean data to help AI systems? How do we monitor the quality of AI-generated outputs? Is our team trained to understand and utilise AI suggestions? Can we scale this AI implementation as we grow? Tools help to integrate AI that suggests feature ideas based on competitor moves, app reviews and support tickets. This makes the backlog smarter and customer-centric. Some platforms let you describe an app idea in English and auto-generate working prototypes with basic logic built-in. This is how non-technical founders build MVPs. AI is now forecasting user mood, purchase intent, engagement drop-offs before they happen. This allows timely interventions, like re-engagement campaigns or UX iterations. How to adopt AI without falling into the trap: Start small: Begin with one area, testing automation or design prediction and expand. Begin with one area, testing automation or design prediction and expand. Training: Even senior devs need guidance to use AI tools effectively. Even senior devs need guidance to use AI tools effectively. Use explainable AI : This ensures transparency, especially for regulated industries. This ensures transparency, especially for regulated industries. Measure ROI: Track time saved, bugs found, conversion improved the value. Forget about AI will replace developers. AI in 2025 has become a co-creator in mobile app development speeding up repetitive tasks, spotting blind spots and bringing fresh ideas but the key decisions are still made by humans. Around high-performing dev teams, we're seeing AI handle: 60% of code generation 80% of first-draft test cases Real-time feedback loops integrated in UX flows And the most successful mobile apps still have resilient Human Creativity behind them. AI gives you speed, personalization and feedback faster than ever but how you define success, who your users are and what problem you're solving still comes from humans. The best apps of 2025 won't just be tech advanced. They'll be emotionally intelligent, easy to use and continuously evolving. To get there, don't just include AI, Build around it. Code and Collaborate with AI then Ship with confidence. TIME BUSINESS NEWS
Cision Canada
29-05-2025
- Business
- Cision Canada
YASH Technologies Achieves the AWS Generative AI Competency
EAST MOLINE, Ill., May 29, 2025 /CNW/ -- YASH Technologies, a global technology and business transformation services provider, announced today that it has achieved the Amazon Web Services (AWS) Generative AI Competency. This specialization recognizes YASH as an AWS Partner that helps customers and the AWS Partner Network (APN) drive the advancement of services, tools, and infrastructure pivotal for implementing generative AI technologies. Achieving the AWS Generative AI Competency differentiates YASH as an AWS Partner with demonstrated technical proficiency and proven customer success supporting enterprises in building scalable, production-grade generative AI solutions tailored to business needs. YASH possesses the experience and expertise shown from successful projects for addressing customer challenges using generative AI solutions as an enabler of their digital transformation strategies for augmenting the customer experience, delivering hyper-personalized and engaging content, streamlining workflows, and delivering actionable results powered by generative AI technology from AWS. "YASH is proud to achieve the AWS Generative AI Competency," said Ashish Maheshwari, Vice President, Global Alliances & Business Head, AWS at YASH Technologies. "Our team is dedicated to helping customers accelerate their generative AI journeys by leveraging the agility, breadth of services, and pace of innovation that AWS provides. This recognition validates our ability to design and deploy enterprise-grade AI solutions that solve real-world business challenges, enabling clients to transition from pilots to production with confidence, speed, and scale." This designation highlights YASH's ability to responsibly drive generative AI adoption by integrating large language models, robust cloud infrastructure, and contextual business use cases. Leveraging AWS services, including Amazon Bedrock, Amazon SageMaker, Amazon CodeWhisperer, and Amazon Q, YASH has developed innovative solutions across industries and enterprise functions. "Generative AI is redefining how organizations operate, compete, and create value," said Nitin Gupta, Global Head, Digital, AI & Cloud Infrastructure Management Services at YASH Technologies. "This designation further enhances our ability to support forward-looking enterprises. By combining our domain-driven approach with powerful GenAI capabilities, we enable customers to adopt AI in a structured, responsible, and accelerated manner and help them become AI-first organizations." This latest achievement strengthens YASH's broader collaboration with AWS. YASH holds AWS competencies in Data and Analytics, Migration and Modernization, DevOps, and Cloud Operations. With the AWS Generative AI Competency, YASH further reinforces its position as a trusted AWS Partner for organizations looking to operationalize AI securely and at scale. About YASH Technologies YASH Technologies focuses on enabling its customers to reimagine their businesses and drive outcome-centric AI-led Digital Transformation. As a global technology integrator and outsourcing partner, YASH combines strategic advisory, technology consulting, and flexible business models to help customers unlock value from their digital journey. Its consultative framework integrates domain expertise, proprietary methodologies, and digital solutions to deliver secure application, cloud, infrastructure, and engineering services. Headquartered in the US with global delivery and sales centers, YASH serves customers across six continents. The company is CMMI DEV V2.0 Level 5, ISO 9001:2015, ISO 27001:2013, and ISO 20000:2011 certified. For more information, visit or email [email protected].
Yahoo
20-02-2025
- Business
- Yahoo
The skill Salesforce's AI boss says is more important than learning to code
Salesforce's AI boss said having agency is "far more important" than learning to code. EVP Jayesh Govindarajan said he defines agency as seeking out a problem and having the drive to solve it. Mark Zuckerberg has a similar hiring philosophy and said he values the ability to "go deep and do one thing really well." For years, "learn to code" was the go-to advice for anyone wanting to break into a tech career — but Salesforce's head of AI says another skill is more valuable these days. "I may be in the minority here, but I think something that's far more essential than learning how to code is having agency," executive vice president Jayesh Govindarajan said in an interview with Business Insider. Govindarajan said that's because Salesforce is building "a system that can pretty much solve anything for you" but "just doesn't know what to solve." "I think far more important than knowing how to code is having that agency and that drive to go get it built out," Govindarajan said. The AI boss gave a hypothetical example of someone trying to solve a problem for College Possible, a nonprofit that helps students prepare for college and receives funding from Salesforce. Govindarajan said that someone could interview a counselor, see what they do on a daily basis, and then use an agentic AI system to "describe what you're trying to build and it'll give you a first draft of the solution." While that first draft may not be perfect, "you go take it to this counselor, have them play with it," and listen to their feedback and any critique, he said. "Then you'd come back and you tweak it again. No code. You'd give it instructions in English. That's very possible," Govindarajan said. The Salesforce exec said someone who has gone through this process has demonstrated two key things. "One, agency to go seek out a problem to solve," Govindarajan said. "And two, learn the toolset — that's a no-code tool set or a low-code tool set — to be able to go get the job done." In that hypothetical example, once the counselor is interested in actually buying the proposed solution, a more experienced coder could then be brought in to sharpen up the edges and fine-tune the software product. Govindarajan's remarks offer a look into how the world of software development and sales is evolving in the age of AI. Since the emergence of AI tools like GitHub Copilot or Amazon CodeWhisperer, a number of coding tasks have been automated, creating uncertainty in a once-stable industry and new challenges for some younger entrants looking to break into software engineering. During Google's third-quarter earnings call in October, Sundar Pichai said over a quarter of new code at the company is generated by AI, although it's still reviewed and accepted by employees. Other tech giants have similarly integrated AI into coding tasks. One Microsoft manager told BI that AI helped him cut down the time he spent on coding tasks by about 70%. Even as coding becomes increasingly automated, some industry leaders believe learning the basics continues to be necessary, arguing that it's more important than ever to understand the fundamentals of technology in order to build on it. Other industry leaders seem to be leaning in the direction that soft skills could be what sets candidates apart. Mark Zuckerberg said in a July 2024 interview with Bloomberg that he believes the most important skill is "learning how to think critically and learning values when you're young." "If people have shown that they can go deep and do one thing really well, then they've probably gained experience in, like, the art of learning something," Zuckerberg said in the interview, discussing what he looks for in job candidates. The CEO said that skill applies to situations that could arise during a career at Meta and it's key to showcase your ability to dive deep and master whatever you're working on. Or, as Govindarajan might put it: using the tools at your disposal to get stuff done. Read the original article on Business Insider
