Cloud-native systems and AI agents are run in fragments — delivery, AIOps, SRE, security, guardrails, and cost each in their own tool. Taksha is the intelligence layer that unifies them — one graph that reasons about cause, not just correlation, across the full lifecycle of cloud-native and agentic systems.
Every layer has its own tool, its own data, its own context — and now AI agents add a fast-growing estate with its own runtime, guardrails, and risk. When an incident hits, the answer is scattered across all of them, and no one sees the lifecycle end to end.
Taksha unifies the fragments into a single intelligence layer: one graph the whole lifecycle reads from. So delivery, reliability, security, guardrails, and cost stop being separate tools — and you can reason about cause across the entire estate.
A product arm built to scale. The first platform, Taksha Orbit, is designed to manage the full lifecycle of cloud-native and agentic systems on a single graph — from continuous delivery through causal AIOps to the CIO's ROI view. Every platform we add extends the same graph.
Five strategic practices that embed with your team and operate the lifecycle with you — from building platforms to running cloud, securing and governing it, mastering data, and setting AI strategy.
Cloud-native systems and AI agents move through the same shape of lifecycle — admitted, made visible, governed, and proven. Taksha begins where your build ends: we don't replace your toolchain, we take ownership of the operating lifecycle once a release or an agent goes live.
The customer keeps CI. Taksha Orbit begins at the operational handoff and owns visibility, governance, recommendation, and lifecycle evidence until retirement.
Agents carry identity, capabilities, and access intent. Taksha Orbit governs registration, evaluation, runtime behaviour, and safe termination with the same rigour as cloud-native services.
Every service, dependency, agent, model, and tool mapped into one graph — the single context the whole lifecycle reads from.
Designed to investigate across service and agent dependencies the way an engineer would — traversing the graph and tracing root cause, instead of ranking correlated alerts.
Compose, register, evaluate, observe, and govern AI agents with the same discipline as cloud-native services.
Policy, safety, and quality evaluation built to run in the pipeline — agent behaviour governed, not guessed.
One context across public cloud and private estate — AWS, Azure, GCP, OCI, and VMware, Nutanix, OpenShift, or BYOC.
Designed to roll the same lifecycle up to the view a board asks for — risk held, value captured, evidence kept.
The name derives from the Sanskrit taksh — to carve and sculpt raw material into form — after Takshashila, one of the world's earliest centres of structured learning.
The intelligence layer that unifies fragmented cloud-native and agentic operations — so the lifecycle you run only gets deeper over time, and the context only gets richer.
Explore the platform, or talk to the engineers who'll help you operate it — across cloud and agents.