Luciq announced a significant expansion of its Agentic Mobile Observability platform, extending agent-driven intelligence across the entire mobile app lifecycle.
The release introduces a coordinated, closed-loop system of AI agents that continuously detect, triage, resolve, and help prevent mobile production issues before they affect end users.
Originally focused on post-release debugging, Luciq’s platform now connects production insight directly to development and release workflows. The result is a shift from reactive observability to proactive reliability, purpose-built for the realities of mobile engineering.
Luciq’s Agentic Mobile Observability platform applies specialized AI agents across the mobile lifecycle. These agents continuously analyze real-world production behavior, prioritize the most impactful issues, and assist teams in resolving problems faster.
“Mobile engineering has unique challenges that general observability tools weren’t designed to solve,” said Moataz Soliman, Co-founder and Chief Technology Officer at Luciq. “With this expansion, Luciq’s agentic systems move beyond observation. They actively work with developers to reduce investigation time and protect app quality as teams ship faster.”
At the core of the expanded platform is a coordinated four-agent system designed to mirror how mobile teams build, ship, and operate apps in production:
- Detect Agent continuously monitors mobile apps to identify silent failures that degrade user experience, including UI hangs, broken interactions, and non-crashing logic errors.
- Triage Agent automatically groups thousands of duplicate bug reports into single, actionable issues, reducing alert noise and developer fatigue.
- Resolve Agent, which powers AI Crash Insights, analyzes metadata across millions of user sessions to surface likely root causes and reproduction steps in seconds.
- Release Agent acts as a production guardrail by analyzing potential regressions during the pull request process, helping teams protect user experience before code is merged.
- Together, these agents form a closed-loop workflow where insights from production continuously inform development and release decisions.
Luciq is also introducing Agentic Instrumentation, a new onboarding experience that allows mobile teams to move from a clean codebase to their first visible issue in the dashboard in under 10 minutes. This significantly reduces the friction and time-to-value traditionally associated with mobile SDK setup.
In addition, Session Replay 2.0 delivers a unified, color-coded timeline that connects user interactions, logs, and network events in chronological order. This visual context helps eliminate the reproducibility gap for complex mobile bugs that are difficult to recreate locally.
Rather than relying on reactive alerts, Luciq continuously prioritizes issues across releases, devices, and user sessions, enabling teams to focus on the problems that matter most to users and the business.
The platform is built for mobile engineering leaders, including VPs of Engineering, CTOs, and platform leads responsible for balancing development velocity, reliability, and governance.
“Agentic Mobile Observability makes observability practical at scale,” said Kenny Johnston, Chief Product Officer at Luciq. “It allows mobile teams to spend less time firefighting and more time building, without sacrificing reliability.
The Latest
Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...
Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...
When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...
If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...
Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...
AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...
Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...
Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...
Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...
In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...