Developer productivity insights platforms (DPIP) measure dashboards. Your DevEx problem is bigger than that.

Gartner recently published their Magic Quadrant for Developer Productivity Insight Platforms (DPIP), evaluating about a dozen standalone DPIP providers, including Uplevel, Jellyfish, DX, and Opsera. Gartner defines DPIP as solutions that provide software engineering leaders with data-driven insights into their teams’ use of time, resources, operational effectiveness and progress on deliverables.

The report raises a few interesting questions that we see the market, Gartner, and even DPIP vendors themselves grappling with- especially as AI adoption accelerates and continues to reshape how we build and ship software.

Key questions for engineering leaders to keep in mind when looking into DPIPs and DevEx:

1. Are measurements and dashboards really valuable without the ability to take action?

The answer is: No. In the opening of the DPIP MQ report, Gartner themselves predict that “by 2028, 80% of DPIP platforms will shift from manager-focused dashboards to developer-focused enablement.” Put plainly: letting developers actually fix what the dashboards surface.

DPIP tools give you visibility. Dashboards and metrics are great, but when you identify a bottleneck or see a standard slipping - then what? You need separate tools to act on it. There’s no workflow layer, no self-service actions, no way to close the loop between seeing the problem and fixing it.

Even the vendors in the MQ report seem to recognize that data alone is not sufficient. Mirroring the platform convergence trend we’re seeing across DevOps, DPIP tools started expanding ‘left’ and ‘right’: adding workflow triggers, CI/CD integrations, forays into agentic solutions, LLM support, intelligent resource optimization, data migration, and more. 

As DevOps and Platform Engineering have shown repeatedly, siloed solutions only go so far. Real impact comes from tying metrics to outcomes in a single platform - for both humans and agents. Organizations don't need another point tool that stops at the chart; they need an end-to-end solution that closes the loop from insight to action across the entire lifecycle. That may fall outside Gartner's DPIP standalone definition - but it's exactly what real-world enterprise engineering demands.

2. Is it enough to enable developers with insights and action, without AI?

The answer is, once more: No. Gartner further predicts that by 2028, 60% of DPIP platforms will “act as foundational context engines, equipping agentic workflows.”

As AI-assisted development accelerates, developers and agents collaborate across the entire SDLC. Realizing that they need to leverage AI reliably, with human-in-the-loop controls and shared governance - organizations have turned to agentic engineering platforms or agentic IDPs like Port to tame AI sprawl, reduce toil, and improve developer productivity. 

With richer, more holistic context than standalone DPIPs, Port customers can easily track the direct impact of AI adoption on service delivery and key metrics like deployment frequency, change failure rate, and standards compliance. They also track when AI-assisted code ships, whether it passes quality gates, or drives incidents up or down. 

But Port doesn’t stop at the chart. Engineering Intelligence metrics feed automation and agentic workflows directly.

Developers get self-service actions to fix what the dashboards surface. Agents go further: when MTTR spikes or deployment frequency drops, they troubleshoot, deploy fixes, smoke-test patches, enforce standards, or escalate - including blocking AI-generated PRs from high-incident repos until patterns are investigated. All with scoped permissions, audit trails, and human-in-the-loop governance balancing AI-driven and deterministic workflows.

In addition, a powerful agent-interface lets platform engineers build using natural language, centrally manage AI agents, skills and MCPs, and easily extend the platform as needs evolve.

3. What this means for your engineering organization? 

Ask yourself: do you want to measure, or do you want to improve? When a metric surfaces a problem, what happens next? Do you have all the information? Are metrics connected to the services and teams that generate them? Can you fix it in the system? Or do you need to investigate and take action in another tool? Do you already use AI or plan to in the future?

Engineering leaders don’t lack dashboards. They lack the ability to act on them. Seeing a problem and fixing it are two different things and the gap between them is where improvement goes to die. At the scale of AI and autonomous action, that gap becomes a chasm – and every unresolved signal is a risk left to grow. 

We see these trends accelerating faster than Gartner predicts. While we agree with their general market assumptions, we don't believe DPIP tools, however expanded, are the right solution to meet the needs of modern software delivery.

4. What this means for your platform architecture?

The 2025 DORA research concluded “Platform quality is the make-or-break variable for AI ROI”. “When platform quality is high, the effect of AI adoption on organizational performance becomes strong and positive. Conversely, when platform quality is low, the effect of AI adoption on organizational performance is negligible”. According to DORA, “a high-quality internal platform acts as the essential distribution and governance layer for AI.  So what are the key requirements to consider for your platform architecture?

You need a single, unified platform:

1) Spanning the entire SDLC

2) Measurement must live where work happens

Unifying engineering intelligence metrics, actions, and agents in a single platform that spans the e2e SDLC matters now more than ever. This is the only design choice that closes the loop between measurement and action at AI-scale. Unlike standalone DPIPs, Port’s Engineering Intelligence capabilities are built into a central e2e platform by design - because without the ability to act on data, a dashboard is just a fancy chart.

You need a unified context lake:

1) Shared across measurement, standards, and actions

2) Shared by AI agents and developers

Reasoning models need grounding- provided by the Context Lake. Without structured context lake they generate 10x unnecessary tokens. Well architected context lake reduce token consumption 3x and improve accuracy 40%.

To maximize impact, Insights and Engineering Intelligence must share the same unified, real-time context that powers your software catalog, scorecards, automations and agentic workflows. When a metric surfaces a problem, the context is already there: the team is known, service owner notified, runbook identified, repo linked. As AI becomes more prevalent across the SDLC, agentic workflows can immediately troubleshoot, escalate, remediate, and confirm resolution - all on the same system, using and updating the same data.

Port's context lake continuously correlates and enriches real-time SDLC data – tools, services, deployments, environments, incidents, dependencies, compliance state,  configurations, policies, and other operational knowledge. 

This becomes the production-grade trust layer AI needs for safe execution at scale. The context lake covers the full picture: who owns each service and team, what is happening across your SDLC in real time, why actions were taken through decision tracing that captures agent reasoning, and how that context flows dynamically to both developers and agents. This is what connects deterministic scorecards to AI reasoning. Scorecards set the rules. Agents reason within them. Humans approve decisions. Every action is auditable. And the context gets richer with every resolution.

OOTB vs DIY Context Lake: 

‍Teams that attempt to DIY Context Engineering typically hit the same wall: split data stores create drift between AI-facing and developer-facing systems, making data impossible to act on at the speed AI demands. The hard-won lesson from platform engineering applies here too: centralize, unify, respect data gravity.

Context engineering requires ongoing metadata management, agent reasoning at scale, and a governance layer that both humans and AI can trust. Knowing how critical a foundation that is, Port comes with the context lake built in — continuously updated, shared across all platform capabilities, and available to both AI agents and the humans who need to observe, govern, or override them. It's a core product capability, not a bolt-on.

The context lake isn't infrastructure you build - it's infrastructure you earn. Port gets teams to value faster, then scales as your needs evolve: from agents that eliminate toil at specific pipeline steps (enriching incident tickets, investigating failures, fixing bugs, summarizing RCAs), to full autonomous resolution from request to deployed PR. With an architecture that’s production-ready from day one, the only limit is your ambition.

How to accelerate your path from insights to better outcomes 

Standalone DPIP tools are now expanding toward capabilities already core to Port's Agentic Engineering Platform. The market is moving in our direction - by design. We built our solution from the ground up for the flexibility, controls and scale that this brave new world demands.

Port continues to invest in Engineering Intelligence as a core capability of the e2e Platform - closing the gap from metrics to outcomes. By unifying a context lake, self-service portal, agentic workflows, orchestration, guardrails, and engineering intelligence in a single platform, Port leads enterprises on the path to autonomous engineering - safely, and at scale.

To see what Engineering Intelligence looks like when it’s built on a platform rather than bolted to one, book a demo or take it for a spin!

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