Hans Eckman's Info-Tech LIVE keynote cuts through the hype and tells developers exactly what they need to do differently — starting today.
AI coding tools were supposed to make developers faster, eliminate ambiguity, and reduce production defects. The data says otherwise. According to research presented by Hans Eckman, Research Fellow at Info-Tech Research Group, at Info-Tech LIVE 2026 this week in Las Vegas, 84% of organizations use AI tools but see no measurable improvement in delivery velocity or business outcomes. Incidents per pull request are up 24%. Only 17% of AI code suggestions are actually adopted.
"The technology isn't what's failing," Eckman said. "It's our understanding of capabilities."
That's actually good news. Because it means the problem is fixable.
Eckman's keynote, titled "Developer Survival Guide in the Age of Agentification," laid out a three-part framework for developers and engineering teams navigating a world where AI agents are writing more code than ever — and where the human role is shifting in ways that most teams haven't caught up with yet.
The Shift: Your Job Has Changed
75% of all new code at Google is now AI-generated, up from 50% just last fall. Info-Tech's own AI in Software Development Survey (May 2026, n=442) found that 42% of developers go from requirement to working code in the same session. Agentic engineering is no longer a future state — it's the current one.
But that doesn't mean developers are less important. It means the job is different. Eckman was direct: the traditional role of a developer — the one primarily defined by writing code — is fading. What's replacing it is a focus on outcomes and solution architecture.
"If you haven't made your intent explicit," Eckman said, "the agent will execute against your gaps rather than your intended goals."
That's the core problem. AI agents don't stop to ask clarifying questions. They don't pause when requirements are vague. They fill gaps, make assumptions, and execute — and if your specifications are loose, the agent's definition of "done" won't match yours.
67% of developers in Info-Tech's survey report that AI-generated code requires more testing than traditionally written code, not less. 51% say AI breaks down on complex or legacy codebases. 46% say the output doesn't pass their quality gates or code review standards.
Developers are still critical. The skill set just needs to evolve.
Eckman used a memorable analogy to describe today's AI tooling landscape. Imagine walking into an ice cream shop expecting three flavors — vanilla, chocolate, strawberry — and instead finding dozens of options that change every week. The choice becomes paralyzing.
"This is what the AI tooling landscape feels like right now," he said.
And it's hurting teams. According to Harvard Business Review (2026), using four or more AI tools simultaneously results in 12% more mental fatigue and 19% more information overload. 45% of frequent AI users report burnout, compared to 35% of non-users. And in what may be the most telling stat: 46% of developers actively distrust AI output, versus only 33% who trust it.
The old answer was to go deep on one tool and master it. That worked until about 2023. Now, most teams have flipped into a mode of constant tool exploration — running proof of concepts, testing new options, chasing the latest release — without ever reaching production-ready outcomes.
Eckman's prescription: flip the script. Spend 90% of your effort on execution with a stable core stack, and limit active tool exploration to 10%. Pick one or two primary coding assistants. Maintain stable 90-day delivery cycles. Keep experimentation structured and separate from production work.
"Heroes don't scale," he said, quoting his practice manager. "That's the temptation we need to avoid in the AI space."
For teams evaluating terminal tools right now, Eckman's research breaks down the three leading options clearly. Claude Code leads on autonomous depth and handles complex multi-file refactors with a 95% first-try correctness rate, with 71% of developers who regularly use AI agents now reaching for it. Codex CLI fits security-sensitive workflows best, with three configurable approval levels. Gemini CLI is the accessible option for budget teams or GCP-native environments, though its free tier is now restricted to Flash models after March 2026. On the IDE side, Cursor remains the most polished for agentic task completion, GitHub Copilot has the broadest ecosystem reach, and Windsurf is the friendliest entry point for teams newer to AI-assisted coding.
The Stakes: Agents Don't Fail Because They're Wrong
This section of Eckman's keynote is where things got concrete — and where the most actionable guidance lives.
The title says it plainly: AI agents don't fail because they're wrong. They fail because the right outcome wasn't designed and prompted. Vague requirements don't slow agents down. They just give agents more room to guess.
Only 12% of AI projects succeed without success metrics defined upfront. Define them clearly, and that number jumps to 54%. That gap is entirely within a developer's control.
Eckman's fix is spec-driven development. Not better prompts — a proper spec file. Written in plain text, before the agent touches anything, a spec has four components:
Role — A single line defining who the agent is and who it's building for. This shapes every subsequent decision the agent makes.
Success Criteria — Testable exit conditions written before work begins. Without these, the agent's definition of done won't match yours. With them, you get reviewable output that stays on track.
Integration Context — What already exists in the codebase. Which components to reuse, which APIs to call, which patterns to follow. This stops agents from rebuilding functionality that already exists or introducing patterns that conflict with your stack.
Constraints — The hardest part. Every line is a decision the agent will make on its own if you don't constrain it first. Failure behaviors like empty states and error states aren't edge cases — they're design decisions. And the "out of scope" line at the end is often the most important one.
"If you'd be annoyed that the agent interpreted something differently than you intended," Eckman said, "your constraints weren't tight enough."
This applies to teams, not just individuals. The same spec discipline that prevents agent chaos also makes your AI practice institutional rather than individual — shared prompting patterns, documented standards, consistent evaluation gates that don't change every time a new tool launches.
Only 20% of engineering teams currently measure AI impact, despite 90% using AI tools. The metrics that matter: cycle time, code quality, failure rate, review time, and technical debt accumulation. You can't improve what you don't measure.
Governance and Security: Validate Before You Ship
The third section of Eckman's survival guide is where the stakes become most visible.
65% of organizations have experienced at least one cybersecurity incident caused by AI agents in the past 12 months, according to the Cloud Security Alliance (2026). 70% of organizations have uncovered flaws in AI-generated code, and one in five of those incidents escalated into serious breaches.
The Amazon Kiro story makes the point vividly. In December 2025, Amazon's autonomous AI agent caused a 13-hour AWS Cost Explorer outage in China by deleting and recreating a production environment due to misconfigured access controls. Amazon attributed it to user error — and technically they're right. The agent did exactly what agents do. It executed. The problem was the lack of guardrails.
"Agents don't stop and ask for permission," Eckman said. "They decide and execute. If you haven't built in checkpoints, you won't know what went wrong until it's in production."
For every agent your team deploys, Eckman frames the security question across three dimensions: What can the agent see? What can the agent do? What can the agent touch? Each of those is a deliberate access control decision, not a default setting.
The answer isn't to remove agents from the workflow. It's to design the human-in-the-loop carefully. Agents are good at speed, consistency, generating code and tests, scanning for vulnerabilities, and mechanical correctness. Humans need to own architecture design, pattern definition, outcome validation, and approval of high-risk actions.
71% of organizations say human oversight is necessary for building public trust in their AI systems. 76% of enterprises already include human-in-the-loop processes to catch errors before deployment.
Three Takeaways
Eckman closed with a clean, practical call to action for every developer in the room:
First, audit your AI stack honestly. Stop exploring and start executing. Pick your core tools, stabilize your delivery cycles, and structure how you evaluate anything new.
Second, define before you build. Write down your success criteria, your integration context, and your constraints before the agent touches a single file. This is the most valuable engineering work you'll do — and it will save weeks of rework.
Third, define where humans are in the loop. At minimum, humans should be reviewing code before every release. Decide upfront where agent autonomy ends and human judgment begins, and make those boundaries explicit in your process.
The age of agentification isn't coming. It's already here. The developers who thrive in it won't be the ones who write the most code. They'll be the ones who design the best outcomes.
Hans Eckman is a Research Fellow in Application Delivery and Management at Info-Tech Research Group. He presented this keynote at Info-Tech LIVE 2026 in Las Vegas, June 8–11.