Are AI Agents the Future of Software Delivery?

The decades-long standard for constructing and distributing software consists of multiple, sequentially cooperative teams.

The responsibilities of different teams include the following:

• Coding: Developers

• Testing: QA Engineers

• Deployment: DevOps Engineers

• Infrastructure: Platform Engineers

• Scheduling: Project Managers

Even modern automation tools have not largely changed the process of software delivery. It has remained a people-centred process, with different teams passing their work alternately.

But that might be changing.

The technology consulting company Endava has introduced an AI agent network that functions across many stages of a software delivery lifecycle. Their system consists of multiple specialised AI agents that work together to perform automation tasks across Development, Testing, Operations, and Delivery. This new network of AI agents is an upgrade from the (legacy) technology of a singular, autonomous AI agent.

Although the announcement is specific to Endava, it is representative of what is going across the software industry. Companies have started to look beyond using AI solely as a tool for coding assistance. Currently, the focus is on the use of AI to support the end-to-end process of software delivery.

Software Delivery Is About More Than Writing Code

Discussions about AI in software development usually start with coding assistants. Most developers know tools like GitHub Copilot, Cursor, and Claude. These tools help generate code, explain functions, and identify errors. While these tools have increased developer productivity, software delivery involves much more than just writing code.

A typical software release includes gathering requirements, planning architecture, developing applications, testing, validating security, setting up infrastructure, deploying, and ongoing monitoring. Each stage often needs different teams, tools, and approval processes. This makes software delivery much more complex than the coding process alone.

This growing complexity is one reason AI agent networks are gaining attention. Instead of relying on one AI assistant for individual developers, organisations can use multiple specialised AI agents that operate at different stages of the software delivery process. By managing specific tasks throughout the delivery pipeline, these agents can help streamline workflows, reduce bottlenecks, and improve overall efficiency.

Speed Isn't the Problem. Clarity Is.

Many software teams aren't limited by how quickly developers can write code. They're limited by coordination. Projects often slow down because of reviews, approvals, testing cycles, infrastructure requests, compliance checks, and deployment processes. These handoffs can create delays even when development work is completed.

AI agent networks aim to reduce some of those bottlenecks by automating repetitive workflow tasks.

Potential applications include the following:

• Creating implementation plans

• Generating test cases

• Reviewing pull requests

• Preparing deployment pipelines

• Monitoring application performance

• Identifying infrastructure issues

• Producing technical documentation

The goal isn't necessarily to replace engineers. The goal is to remove friction from the software delivery process so teams can spend more time solving problems and less time managing routine tasks.

The Industry's Next Focus: Workflow Productivity

The first wave of AI adoption aimed to boost developer productivity. Organisations used AI tools to help engineers write code more quickly, cut down on repetitive tasks, and speed up development. Although these benefits were significant, many companies soon found that faster coding does not guarantee quicker software delivery. Every release still needs to go through security reviews, compliance checks, testing cycles, deployment approvals, and operational validations before it reaches production.

Now, the focus is shifting from coding efficiency to workflow efficiency. Business leaders are increasingly asking how software can move from idea to production more quickly instead of just how developers can code faster. This has prompted many organisations to look into AI systems that can automate parts of the wider software delivery process. The next big competitive edge may come not from coding more efficiently, but from simplifying the workflows, approvals, and operational processes that often delay releases.

The Trend That Has Enterprises on High Alert

For enterprises managing hundreds of applications, even small delays can have a significant impact.

A few hours saved on each release can translate into substantial gains across large engineering organisations.

As a result, many companies are exploring ways to specialise.

• Deliver software more quickly

• Reduce operational overhead

• Improve consistency across releases

• Increase engineering productivity

• Strengthen governance and visibility

AI agent networks offer a potential path toward achieving those goals.

By assigning specific responsibilities to specialized agents, organisations may be able to automate processes that previously required extensive coordination between teams.

Automation Doesn't Remove the Need for Governance

Despite the growing excitement surrounding autonomous systems, most enterprises are not ready to hand over software delivery entirely to AI. The potential benefits are significant, but so are the risks. AI-generated actions can still introduce security vulnerabilities, compliance issues, infrastructure misconfigurations, performance bottlenecks, and reliability concerns that could impact critical business operations.

For this reason, many organisations are adopting a human-in-the-loop approach. In this model, AI agents can automate tasks, generate recommendations, and assist with decision-making, while experienced engineers remain responsible for reviewing, validating, and approving critical actions. As AI systems become more capable and take on greater responsibilities across the software delivery lifecycle, strong governance, oversight, and accountability will become increasingly important to ensure both speed and reliability.

Developers May Become Workflow Orchestrators

One of the most interesting long-term impacts of AI agents is how engineering roles could evolve. Developers won't stop coding anytime soon.

However, their responsibilities may gradually shift toward supervising intelligent systems that handle routine execution.

Future engineers may spend more time:

• Reviewing AI-generated outputs

• Designing development workflows

• Coordinating multiple AI agents

• Establishing governance frameworks

• Making high-level architectural decisions

In other words, the value of engineering expertise doesn't disappear. It moves higher up the decision-making chain. The engineers who can effectively manage both software systems and AI-driven workflows may become some of the most valuable professionals in the industry.

Looking Ahead

Another sign that the software industry is entering a new phase of AI adoption is Endava's AI agent network. Until recently, most AI tools were focused on helping developers write code faster and improve individual productivity. Today, the conversation is expanding beyond code generation to the possibility of automating larger portions of the software delivery lifecycle.

Whether AI agent networks become a standard part of engineering organisations remains to be seen. However, it is becoming increasingly clear that software teams are beginning to envision a future where humans and AI agents work together to build, test, deploy, and maintain applications.

Organisations that learn how to effectively manage this collaboration may be better positioned to improve efficiency, accelerate delivery, and adapt to the next generation of software development practices.

Frequently Asked Questions

1. What is an AI agent network?

An AI agent network is a group of specialised AI agents that collaborate to perform complex tasks. In software engineering, these agents may handle development, testing, deployment, monitoring, documentation, and operational workflows.

2. Why are companies investing in AI-driven software delivery?

Organisations want to reduce bottlenecks, accelerate release cycles, improve consistency, and increase engineering efficiency. AI agents offer a way to automate repetitive workflow tasks while allowing teams to focus on higher-value work.

3. How does Workfall help companies prepare for AI-driven engineering?

Workfall helps organisations connect with experienced developers, DevOps engineers, platform specialists, cloud professionals, and AI-focused talent who can build, manage, and scale modern software delivery systems in an increasingly AI-powered world.