Anyone can build software now — and it’s causing hell for developers

It’s time to rethink the software development lifecycle for AI agents

May Habib | June 26, 2025

If you’re building AI agents in enterprise, you’ve probably noticed two worlds colliding. Here’s what’s happening: business users are spinning up AI agents left and right, feeling like they can build anything. Meanwhile, developers are stuck being the ‘reality police’ — trying to make these things actually work in production while secretly wondering if they’re automating themselves out of a job.

At the end of the day, many devs feel like they go through all this chaos … only to end up with a solution not that different from the software they’ve built before.

From where we sit, however, developers aren’t going anywhere. What’s more, to realize the potential of AI agents, business users and developers need to work hand in hand. So, where do we go from here?

After deploying over 5,000 agentic systems at leading enterprises like Vanguard, Salesforce, and Uber, we learned some important lessons about what works AND what doesn’t.

Reimagining the SDLC for an agentic world

In the past two decades, enterprises mastered the software development lifecycle (SDLC). Methods and practices like Agile, DevOps, and DORA brought speed, structure, and predictability to the work of crafting new tools and platforms.

Generative AI threw a curveball into this world, demanding that organizations learn to work with systems that, by their very nature, are non-deterministic. Agentic AI disrupted that playbook further.

The tech industry has begun to align around emerging standards like MCP and A2A in the last six months. What we don’t have at the moment is a clear methodology for‌ creating and sustaining these incredibly powerful new tools in enterprise environments.

What we need is basically ‘Agile for Agents’ — a playbook that actually works for building, shipping, and maintaining agents at scale. Internally we’ve started referring to this as the Agent Development Lifecycle (ADLC.)

Six core principles of the ADLC

Over the last few months, we’ve started to put together our perspective on how to address these challenges. We’ve come up with six core principles that form the backbone of our working methodology:

1. Build for outcomes, not requirements

In the SDLC, everything was built around requirements: businesses write a spec, hands it off, and hopes it holds through dev cycles. In agentic development, it starts with outcomes. That’s a fundamental shift...

2. Right-size solutions with agentic alignment

The biggest mistake in enterprise AI? Building autonomous agents when you need simple automation. The key is matching the level of autonomy to the actual problem — not every business process needs an agent that can reason, plan, and act independently...

3. Put process owners in the driver’s seat

The SDLC puts developers and PMs in the driver’s seat. But with agents? The people who actually know the workflow need to be hands-on from day one...

4. Shift from process design to behavior design

In the SDLC, you design deterministic processes — a series of predictable steps, coded against a well-defined spec. Input in, output out. But with agents, you’re not defining step-by-step logic anymore, you’re shaping agent behavior....

5. Evolve from development mindset to scaling mindset

A lot of teams today are stuck in development mode. They’re racing to build something that works, focused on one-off use cases...

6. Move from QA and testing to evals and fine-tuning

In traditional software, QA is objective. You write test cases, check them off a UAT list, and validate whether the code behaves exactly as expected...

Help us imagine the future

As an industry, we’ve spent the last 30 years mastering software development. We turned software into a well-oiled machine: predictable, structured, and fast to ship...

If you’re finishing this piece and nodding your head in agreement, we want to hear from you! To make the ADLC a standard that can be widely adopted, it needs validation. Just as DORA metrics proved their worth through public review and testing, we hope to work with partners, customers, and peers to get real data behind the strategies we’re proposing.