DeveloperWeek 2026: Making AI tools that are actually good

DeveloperWeek 2026 may have been shorter than its name suggests, but it packed a punch with insights into the real-world challenges developers face with AI tools. While there were no flashy product launches or keynote spectacles, the event zeroed in on the everyday work of developers—and the burning question: are AI tools actually good?

The usability problem: AI that takes us on a wild ride

The biggest takeaway from the conference was that many AI tools aren't built for actual human use. As Caren Cioffi from Agenda Hero pointed out in her session, most AI tools prioritize speed and efficiency over usability. The result? Tools that feel less like extensions of our will and more like unpredictable creative partners.

Cioffi shared a painfully relatable story about struggling with an AI image generator. The tool produced an almost-right image, but every attempt to fix small issues made things worse. This happens because AI image generation is essentially a black box—you feed it a prompt and hope for the best, but each output is slightly different due to the non-deterministic nature of AI.

This unpredictability becomes exponentially frustrating when dealing with critical codebases. The solution? Give humans back agency. Instead of forcing users to regenerate entire outputs, usable AI tools should allow for small, targeted edits directly in the UI. When users can shape AI outputs to their needs, adoption follows naturally.

Context is the gamechanger

If there was a buzzword bingo at DeveloperWeek, "context" would have been the winning square. Multiple speakers emphasized that AI's effectiveness is directly tied to the quality and relevance of its training data.

For developers, this means AI coding tools without company-specific context produce code that doesn't match internal standards, architecture, or workflows. The result? Developers spend time cleaning up and reorganizing AI-generated code—essentially becoming janitorial staff for their own tools.

Jody Bailey, Stack Overflow's Chief Product and Technology Officer, called context "the gamechanger" for AI tools. He explained that out-of-the-box AI trained on public data can never deliver true productivity gains for organizations with specific workflows and guardrails.

Senior Director Lena Hall from Akamai put it bluntly: "Context is all you need." The solution isn't just about model intelligence—it's about information design. Companies need to feed industry and company-specific context to their AI tools, either beforehand or during the logic process.

Solutions mentioned included MCP servers, A2A protocols, and advanced RAG (Retrieval Augmented Generation) systems. Even design tools like Figma are getting in on the action, adding context through user-inputted brand kits and copy specifications.

The trust deficit and why context matters

A recurring theme was that developers don't fully trust AI tools. When AI produces incorrect answers or code that's "almost right," developers waste time reworking outputs. This erodes the promised productivity gains and creates technical debt as small issues compound.

Hall argued that instead of treating these as model intelligence problems, we should view them as information design challenges. By including domain expertise during logic formation rather than forcing humans to check AI's work after the fact, we can create more reliable and trustworthy AI systems.

Interoperability: Making AI agents actually work together

Nazrul Islam, IBM's Chief Architect for AI, highlighted another critical challenge: interoperability. Building millions of agents isn't enough—they need to work together like a well-oiled machine.

Islam painted a picture of agentic systems that function like a gold-medal relay team: a sales AI closes a deal and passes the baton to finance, which creates a forecast and passes it to customer success, and so on. But achieving this requires overcoming significant hurdles.

The current state of distributed systems—spanning SaaS, public cloud, and on-prem environments—wasn't designed with AI agent interoperability in mind. Creating connectors and governance for these systems is one of the main difficulties leaders face when trying to automate entire workflows.

Islam's advice for building effective agentic teams includes:

• Taking inventory of existing capabilities like APIs and events
• Normalizing access for models through MCP and A2A
• Creating observable and auditable governance for interactions
• Mapping out cross-system journeys
• Building AI teams with these considerations in mind

With proper interoperability, agents could even "discover" each other, creating new pathways for automation and information sharing.

The junior developer dilemma

As the resident Gen Z writer for Stack Overflow, I couldn't ignore one of the most pressing questions for my generation: how can junior developers get jobs in a market where AI code generators seem to do their work?

The answer, according to Romanian IT academy Coders Lab, is that traditional entry paths are disappearing. Internships and on-the-job learning are becoming rare. Junior developers must now prove they're more valuable than AI tools.

Coders Lab's solution is to give junior developers actual client work under senior mentorship. This approach allows them to showcase technical skills, develop soft skills like communication and collaboration, and receive guidance from experienced professionals.

The presence of students at DeveloperWeek—whether at the hackathon or networking on the expo floor—highlighted that young developers recognize the need to distinguish themselves through physical presence in communities and conversations. In a world where AI can generate similar (or sometimes better) code, human connection and demonstrated value become crucial differentiators.

The road ahead

DeveloperWeek 2026 validated many ongoing conversations in the tech community: AI tools are promising but not yet good enough, they desperately need context to be truly helpful, and achieving true automation requires more sophisticated architectures.

The reassuring takeaway is that there's still plenty of work to be done—and therefore, still plenty of need for human developers. The tech industry hasn't yet realized AI's full potential, which means developers remain essential to bridging the gap between AI's capabilities and real-world needs.

As we move forward, the focus needs to shift from building more powerful AI models to creating AI tools that are actually usable, contextually aware, and capable of working together seamlessly. Only then will we begin to see the productivity gains that AI has long promised.