AI agents are redefining software development—but they’re flying blind without observability

Imagine a team of AI agents building, deploying, and running software at machine speed—yet unable to see what’s happening in production. This is the new reality for enterprise technology leaders. As one Fortune 500 CTO told us, “Speed is now the primary driver of innovation, forcing organizations to rethink processes, compliance, and roles; it’s a necessity for innovation teams.”

Observability—real-time visibility into how software behaves in production—has become the critical enabler for both human-led and agent-led teams. Without it, AI agents are powerful but blind.

Key executive insights

1. Software production is being redefined by AI agents. This transformation is a structural shift, not a trend.
2. The world is bimodal again. Human-led and agent-led environments coexist.
3. AI agents are powerful but blind. Without rich context from production, they cannot deliver reliably.
4. Observability is a crucial enabler to gradually transform from human-led to agent-led operations. Observability is what allows organizations to industrialize software delivery with confidence.
5. The new KPI for agent-led teams is the percentage of human intervention required. The lower the number, the better the AI is working.

The market reality: A bimodal world

Organizations are accelerating AI adoption not because it is trendy, but because it is existential. Companies that fail to transform risk are being outpaced by competitors that can deliver software faster, cheaper, and at higher quality. CTOs and CIOs are making statements like “speed over compliance” not out of recklessness, but because they recognize that without radical acceleration, their businesses face disruption.

At the frontier of this shift is a fundamentally new way of building software: AI-first development. In these environments, 100% of coding, testing, deployment, operations, bug fixing, and optimization are performed by AI agents. The human role shifts to specification, goal setting, supervision, and correction. Intellectual property moves from the code to the specification—code becomes a generated artifact, not the source of truth. With a complete, well-architected spec, agents can fully rebuild the software from it again.

This creates a bimodal operating environment:

• Human-led teams—the majority today—are existing operations, SREs, and developers augmenting their workflows with AI. They follow the traditional SDLC, increasingly supported by AI agents that auto-prevent, auto-remediate, and auto-optimize, which reduces manual effort and achieves more with the same resources.
• Agent-led teams—growing fast—are innovation groups operating in full AI development life cycle (AIDLC) mode. Swarms of AI agents build, deploy, and run software end-to-end. Humans write specifications and intent, not code. For these teams, the KPI is no longer “how many story points were solved?” but “what percentage of human intervention is required?”

Observability enables a reliable transition to autonomous operations

In the early 2010s, a similar bimodal pattern emerged with cloud: one team running thousands of servers on-premises, another in stealth mode on AWS. The pattern is repeating now with AI.

Why not switch everything to agent-led right away? Because existing systems follow processes, compliance, and technology stacks that can’t be immediately automated in an AI-first way. Moreover, it’s too risky to move all business-critical systems simultaneously. The safer path: start with an innovation team, build less critical applications first, and only when those are successful and trusted, begin migrating more of the business-critical services.

New foundation models that arrived in early 2026 have accelerated the path to fully autonomous operations, making agent-led teams realistic at small scale today, with large scale within sight. These systems focus on AI-first software generation first, with a clear goal to eventually master operational challenges (resilience, performance, scale, security) entirely with agents as well.

Observability plays a critical role not only in making both modes work reliably, but also in enabling the transformation from the first mode to the second. The context observability provides—understanding existing system behavior, dependencies, and requirements—is exactly what agents need to create the reliable and scalable software. Observability is what makes both modes work, and it is the critical bridge between them.

The core problem: AI agents are blind

AI agents can code, deploy, refactor, and operate software faster than humans ever could. But there is one thing AI cannot do without help: AI has no awareness of what happens in production. It’s blind to the real world: without real-time feedback from running software—in development and production —agents make decisions without context and without understanding their consequences. They operate at speed, but without sight.

77% of IT teams still lack full visibility across hybrid environments (IBM Institute for Business Value, 2025). If you can’t see it, you can’t scale it. Observability is not optional for AI-first operations, it’s a prerequisite.

The Dynatrace response: Real-time observability for both worlds

Dynatrace addresses both sides of this bimodal reality: a complementary response to the two speeds at which enterprises now operate.

For human-led teams: Autonomous operations at scale

This year, Dynatrace launched Dynatrace Intelligence: a full agentic operations system that orchestrates dozens of agents that auto-prevent, auto-remediate, and auto-optimize across site reliability, development, and application security. These AI agents deliver the following value in production:

• SRE Agent: Kubernetes troubleshooting, infrastructure optimization, and automated incident resolution – reducing mean time to resolution at scale.
• Developer Agent: Surfaces production context during deployment, validates changes, and prevents issues before they reach customers.
• Security Agent: Identifies vulnerabilities, triages threats, and accelerates security response, all in real time.

The deterministic foundation underneath: what separates Dynatrace agents from others is its deterministic foundation: real-time, full-stack, and cross-model root-cause analysis, anomaly detection, and forecasting, all grounded by data in a unified, purpose-built data lakehouse that delivers accurate, contextual answers from exabytes of information. This is not AI that guesses; it’s AI that reasons from facts. Benchmarks from internal testing and observed customer use cases: 12× higher success rate in SRE use cases, 3× faster problem resolution, 2.5× lower token cost.

Ecosystem integrations that extend intelligence beyond the platform: Dynatrace Intelligence extends into third-party tools to drive autonomous actions across development, SRE, and ITOps workflows.

For agent-led teams: develop and run software reliably

Dynatrace enables AI-first teams to let swarms of agents build and run software reliably, providing real-world awareness from observability, run-time context across development, security, and operations, and self-optimization toward SLAs, cost, and resilience. Key capabilities include:

• Agentic observability: Closed-loop autonomous operations where observability agents coordinate with coding and deployment agents to self-heal.
• AI and cloud observability: Full-stack visibility across cloud infrastructure and AI workloads, covering resilience, performance, security, user experience, and LLM evaluations to assess the quality and reliability of agent outputs, helping identify potential inaccuracies, hallucinations, or risks.
• AI data lakehouse (Grail): Real-time context engine that provides long-term memory for agent decisions—sub-second, API-native, at an exabyte scale.

The goal: a closed loop where agents detect issues, resolve them, and ship the fix—autonomously, 24/7.

Dynatrace is on the same bimodal journey – our entire business runs on Dynatrace Intelligence in human-led mode, with agents taking over more tasks continuously, while our AI-first offering and new services are built and operated entirely by agent swarms, using our own observability to close the feedback loop.

Different approaches – unified platform

Across the platform, Dynatrace delivers end-to-end, full-stack visibility across cloud infrastructure, applications, and AI workloads, including agent behavior, decision paths, and cost, along with governance at machine scale. These capabilities serve human-led and agent-led teams differently, but from the same unified platform.

The measure of success in software delivery is shifting from human productivity metrics to a new KPI: the percentage of human intervention required. Observability is what makes that progress possible. The question for every technology leader is no longer whether to adopt AI-first, but how quickly they can close the visibility gap before competitors do to drive massive growth in innovation and productivity.