BigPanda acquires Velocity to push agentic AI deeper into IT Operations
BigPanda has acquired Velocity, an AI-powered SRE company known for reliability engineering and incident response. The move expands BigPanda's leadership and accelerates delivery of automated detection and response across enterprise environments.
The goal is straightforward: reduce manual work, improve reliability, and lower operational cost by moving from human-led reaction to proactive, reasoning-driven automation.
Why this matters for Operations
Enterprises spend more than $250 billion every year on manual IT operations processes - detection, response, and prevention. That spend is tied to repeatable, high-touch tasks that AI can now handle with guardrails and oversight.
BigPanda's platform already automates Tier 1 tasks, supports SRE and major incident response teams, and predicts and resolves incidents before users feel it. With Velocity, expect faster time-to-value for detection and response and a stronger path to measurable MTTR and noise reductions.
What Velocity adds
- SRE-first engineering muscle and tooling built for significant incident response.
- Operational experience that fits BigPanda's approach to reasoning-based automation.
- Leadership: Velocity founder and CEO Tal Kain joins as VP of AI Detection and Response. His background includes scaling engineering orgs, AI-driven automation, and experience from Israel's Unit 8200.
What to expect in your stack
- Smarter correlation and detection that cuts alert volume without hiding real risk.
- Automated runbooks with clear human handoff and auditability.
- Faster incident triage, classification, and response workflows across on-call, NOC, and SRE.
- Better service resilience with lower operational overhead.
As the two teams integrate, BigPanda aims to enhance its AI Detection and Response solution to reduce resolution times and improve service stability - without adding headcount.
Practical steps for Ops leaders
- Map current Tier 1 workloads (alert triage, deduplication, routing, enrichment) and target them for automation in the next quarter.
- Instrument baseline metrics: alert volume per service, MTTD, MTTR, false-positive rate, and engineer time spent per incident.
- Define guardrails: when automation acts, when it suggests, and when it escalates to humans.
- Align runbooks to business impact: prioritize services by revenue and user impact, then automate in that order.
- Require vendor transparency: data sources supported, model reasoning visibility, rollback paths, and compliance logging.
Questions to ask your team and vendors
- Which signals do we trust for automated action (logs, metrics, traces, tickets, topology)?
- How will we test automation safely (staging, shadow mode, canary policies)?
- Can we quantify a 90-day ROI (reduced pages, faster resolution, fewer handoffs)?
- How does the system learn from post-incident reviews to improve suggestions?
Key quotes
Assaf Resnick, CEO and Co-Founder, BigPanda:
"BigPanda is defining what Agentic IT Operations looks like in the enterprise, and our customers are already realizing powerful and measurable outcomes. With Velocity, we're accelerating that vision with world-class talent, proven technology, and shared belief in how agentic AI will redefine IT Operations."
Tal Kain, Founder and CEO of Velocity, Incoming VP of AI Detection and Response at BigPanda:
"Velocity was built on the conviction that engineers should focus on solving big problems, not chasing alerts. Combining Velocity's innovation with BigPanda's reach, scale, and data foundation, we're able to deliver agentic workflows and AI-native tools so teams can move faster, stay focused, and deliver reliability at scale."
Context for SRE and Ops teams
- Agentic AI aligns well with SRE principles: automate toil, reduce cognitive load, and protect error budgets. For background, see Google's SRE practices here.
- Expect closer integration across observability, incident management, and collaboration layers, with reasoning that explains "why" an action is taken - not just "what."
Skill up your team
If you're planning a rollout of AI-driven operations, upskilling the team reduces friction and speeds adoption. Explore role-based AI learning paths here.
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