Frequently Asked Questions

DataAgent is an autonomous SRE and remediation platform: it understands the live state of your application and infrastructure, detects problems, identifies what caused them, and can take verified corrective action. It does four things: autonomous detection and remediation, AI-powered autonomous SRE, cost-efficient observability, and it works alongside the tools you already run, such as Datadog and Grafana. The goal is to move from monitoring and alerting to understanding, acting and recovering autonomously.

Remediation-first means DataAgent restores the service before it finishes the investigation. Our philosophy is "fix first, investigate later": stabilise with a verified corrective action, then run the deeper root-cause analysis once the system is stable. It is the difference between infrastructure that tells you something is wrong and infrastructure that does something about it.

DataAgent can safely handle the repetitive, well-understood incidents that make up most of an SRE team's daily load, while complex, sensitive or ambiguous incidents stay with a human. In our experience the split is roughly 80/20: about 80% are recurring operational issues where the right action is well understood, and about 20% need human judgment in the loop.

On the infrastructure side, that covers configuration issues (resource limits set too low, misconfigured health probes, wrong image tags or registry credentials, missing environment variables, port mismatches), scaling and capacity (insufficient replicas, HPA misconfiguration, node resource pressure), deployment problems (rolling back a release that is clearly causing crashes, stuck deployments, image-pull failures), and permission and infrastructure issues (missing ServiceAccounts, IRSA configuration problems, NetworkPolicies blocking traffic, PVC binding failures).

Higher-risk actions such as mass deletion, disabling security controls or exposing secrets always require human judgment and confirmation. DataAgent is built to take the repetitive operational work off the team and leave the decisions that carry real risk with people.

DataAgent understands both the infrastructure and the application running on top of it, and the relationship between them. Most infrastructure automation stops at the Kubernetes or cloud layer, leaving application issues to an engineer. DataAgent detects latency instability, unexpected behaviour changes and regressions introduced by a recent release, and analyses error patterns in the context of the application's data flow, topology, dependencies, architecture and runtime. That is what lets it connect an infrastructure condition to an application symptom and fix problems that cross the boundary.

They are the six stages of DataAgent's remediation loop, each owning one step from detection to learning. PULSE is thin in-cluster detection; TIDE is the living topology graph that adds context; SURGE runs root-cause diagnosis; STEER is the trust gate that decides whether a fix may run automatically; RAPS executes the verified remediation; and WAKE learns from the outcome to improve future decisions.

The Fault Ladder is DataAgent's model for how a single fault progresses from detection to verified resolution, one stage at a time. A fault is detected, enriched with topology, diagnosed to a root cause, gated for trust, acted on with a verified playbook, and then used to improve the system.

TIDE is a live topology graph built from your cluster's real state, not a manually maintained inventory like a CMDB or service catalog. It reflects current dependencies, deployments and configuration drift as they are, so diagnosis runs against what is actually happening rather than a record that can go stale.

DataAgent sits alongside your existing monitoring and alerting stack, including Datadog and Grafana; it does not replace it. It adds a diagnosis-and-remediation layer on top of the signals you already collect, with no rip-and-replace required.

PULSE detects from in-cluster golden signals and anomalies without requiring you to instrument your application code. It watches the state and behaviour already visible in the cluster, so onboarding does not depend on a code-instrumentation project.

DataAgent diagnoses from topology and control-plane evidence, what changed and what depends on what, rather than from the symptom alone. Because it reasons over the living topology graph, it can trace a failing symptom back to the structural change that caused it.

Phase 1 stabilises the service first; Phase 2 runs the deeper root-cause analysis afterward. This is "fix first, investigate later" in practice: the circuit-breaker restores service quickly, and the full RCA then happens without keeping the incident open while diagnosis completes.

Yes. DataAgent produces an evidence chain that shows how it reached a diagnosis and a fix. Every step from detection to remediation is traceable rather than an opaque output.

DataAgent runs six stages end to end: Detect (PULSE), Enrich (TIDE), Diagnose (SURGE), Gate (STEER), Act (RAPS), and Learn (WAKE). It detects the fault, adds topology context, diagnoses the cause, checks whether it is trusted to act, executes a verified remediation, and learns from the result.

DataAgent detects issues through PULSE, its low-data, in-cluster monitoring of golden signals and anomalies. Detection runs inside the cluster rather than depending on shipping large volumes of telemetry elsewhere first.

TIDE pulls the fault's surrounding topology, dependencies, recent deployments and configuration drift, into its context. This is what lets DataAgent reason about a fault in relation to everything it touches, not in isolation.

SURGE runs the root-cause analysis over the enriched topology to produce an evidence chain. It reasons across what changed and what depends on it to move from a symptom to an explained cause.

STEER decides, by applying your trust thresholds to the diagnosis before any action runs. If a fix has not yet earned trust for that situation, STEER routes it to a human instead of acting.

RAPS executes a verified remediation playbook, not an ad-hoc generated script. The action taken is a known, tested remediation rather than an improvised one.

DataAgent improves after each incident through WAKE, which learns from verified outcomes. Those outcomes feed back into future diagnosis and trust decisions so the system compounds what it has already proven.

A copilot explains your incident faster; DataAgent diagnoses and prepares a verified, trust-gated fix. The difference is action: an assistant hands a human a summary, while DataAgent produces a remediation that is gated and executable.

Coding assistants reason over code, not over your live production topology and control-plane state. DataAgent diagnoses from what is actually running and changing in the cluster, which is where most production root causes live.

Topology-driven remediation means DataAgent decides what to fix based on how your system is actually connected, not on the symptom in isolation. It uses the live dependency and change graph to target the structural cause.

A chat assistant has no trust gate, no verified playbook, and no controlled blast radius, which is what safe production action requires. DataAgent adds exactly those controls: trust-gating, verified remediation, and human routing when a fix is not yet trusted.

DataAgent is a remediation-first autonomous SRE system: it detects, diagnoses, and can act on faults under trust controls. It is built around closing the loop on production faults, not around answering questions about them.

Most of the market puts AI agents on top of existing observability pipelines, which can mean more telemetry, more data movement and higher cost. DataAgent brings the intelligence to where the infrastructure and applications actually run: it analyses the environment in place, diagnoses from topology and control-plane state, and resolves issues under trust-gated, verified remediation, without continuously exporting telemetry to an external AI service.

DataAgent only acts autonomously where you have granted it trust for that specific kind of fault; everything else routes to a human. Autonomy is gated by STEER against thresholds you control, so unproven actions do not run on their own. You can start in read-only mode and enable autonomous action only after seeing the fault-coverage report.

The Trust Ladder is how DataAgent earns autonomy one fault class at a time, only after it has been verified correct on that class. Until a fix type is proven and trusted, it stays human-approved rather than automatic.

Yes. You can require human approval for all actions in a chosen environment such as production. Trust thresholds are set per environment, so you decide where DataAgent may act and where it must ask.

High-risk actions such as mass deletion, disabling security controls or exposing secrets always require human judgment and confirmation. Beyond those, DataAgent is restricted to verified remediation playbooks under your trust thresholds, and anything outside them routes to a human. The goal is not to remove humans from SRE; it is to remove humans from repetitive operational work that machines can safely handle.

Guardrails are set per environment, so you can allow broader autonomy in staging and tighter, approval-gated behaviour in production.

DataAgent is built to analyse data in place inside your environment rather than exporting it. Distributed agents analyse the system locally and can resolve issues directly inside your environment.

DataAgent does not export large volumes of logs and telemetry to public or external AI services. It brings intelligence to the environment where the application and infrastructure run, so analysis happens locally and only what a fault requires is used. This reduces data movement and token consumption, and turns AI into a mechanism for reducing observability cost rather than adding another cost layer.

Read-in-place means DataAgent analyses your state where it already lives instead of shipping a copy out to an external service to process. A typical integration forwards telemetry to a vendor's cloud; DataAgent is designed to keep analysis local to your environment.

DataAgent operates on a least-privilege basis scoped to what detection and diagnosis require, and can start in read-only mode.

DataAgent is built secure-by-design, with analysis kept inside your environment. Formal certification status is available on request.

DataAgent is designed to diagnose from infrastructure state and topology, not to read application secrets or PII.

Every action DataAgent takes is logged with the evidence chain and the trust decision behind it, so it can be audited after the fact.

You can run DataAgent in read-only mode first and measure the value before giving it permission to act. The read-only deployment produces a fault-coverage report showing which problems DataAgent can detect, understand and potentially remediate in your environment. It also maps the telemetry your existing tools collect, identifies overlap and redundancy, and produces a potential observability cost-savings analysis. You see what we can fix and what you can potentially save before you trust us with autonomous actions.

A fault-coverage report is the output of a read-only DataAgent deployment: a breakdown of which incident types in your environment DataAgent can detect, understand and potentially remediate today, and which still need a human. It is the basis for deciding, one fault class at a time, where to enable autonomous action.

Yes. DataAgent can start on a single cluster, in read-only mode, before any broader rollout. Starting narrow is the low-commitment way to see whether its diagnosis and coverage are useful to how your team already works.

The design-partner program is a working relationship where your team helps validate DataAgent against real operational problems in your environment and shapes the next generation of capabilities. It starts with a read-only deployment and a fault-coverage report.

DataAgent is typically owned by the platform or SRE team, with security involved for the review.

DataAgent reads data in place instead of shipping a second, paid copy of your telemetry elsewhere to analyse it. It analyses information inside your environment and focuses on what is actually needed to understand and resolve faults, which reduces the volume you pay to collect, store and index. Depending on the environment and current telemetry footprint, we see potential for up to 90% reduction in observability costs.

The observability double-bill is paying once to generate your telemetry and again to ship, store and index a copy of it for analysis. DataAgent avoids it by reading state in place rather than duplicating your data into another platform.

DataAgent is designed to sit alongside your existing observability platform, not to force its removal. Whether you reduce spend elsewhere is your decision; DataAgent does not require ripping out what you already run.

DataAgent uses a value-based pricing model rather than a per-host telemetry meter. Exact pricing is scoped per environment.

DataAgent targets lower MTTR by restoring service first and diagnosing after, and lower observability cost by reading data in place. We are early in our customer journey and do not publish ROI figures yet; our goal is to publish industry benchmarks for measurable DataAgent ROI by mid-2027, once we have a large enough dataset. Until then, the read-only deployment gives you your own numbers before you commit.

No. DataAgent is designed to reduce what you pay to duplicate, not the visibility you depend on. It reads the state needed for diagnosis in place, so the signals you rely on remain available.

DataAgent reduces time-to-resolution by restoring service first and running the deep diagnosis afterward, so the service is back before the full RCA completes. We do not publish a specific MTTR figure yet; ROI benchmarks are planned for mid-2027.

"Auto-remediated" means DataAgent executed a verified fix under its trust controls; "resolved" means the incident was closed, whether by DataAgent or a human. The distinction keeps autonomous action reporting honest and separate from overall closure.

DataAgent takes on detection, diagnosis and verified remediation of recurring faults, reducing the manual investigation load on your team. The aim is to remove humans from repetitive operational work that machines can safely handle, not to add another dashboard to watch.

DataAgent grounds its diagnosis in topology and control-plane evidence and exposes the evidence chain, so its reasoning is inspectable.

Yes. DataAgent verifies the outcome of a remediation and feeds that result back through WAKE. Verification is part of the loop, not a separate manual check.

DataAgent learns from each verified outcome through WAKE so repeat faults can be handled more directly over time.

No. DataAgent is designed to run alongside your existing observability stack with no rip-and-replace. It adds a diagnosis-and-remediation layer on top of what you already collect.

DataAgent is built for Kubernetes on AWS.

DataAgent is designed to install without a code-instrumentation project, so it can start reading cluster state quickly.

No. DataAgent is designed to work from cluster and control-plane state without requiring you to change or instrument application code.

In DataAgent, observability is a feature, not the product. A traditional observability platform tells you something broke and helps you investigate; DataAgent understands the live environment, identifies the likely root cause and can take verified, trust-gated corrective action. Observability watches and reports, while DataAgent closes the loop on the fault.

DataAgent is a layer on top of your observability platform, not a replacement for it. It uses the signals you already have and adds diagnosis and remediation.

DataAgent is built for teams running Kubernetes on AWS with an existing observability stack.

DataAgent's value is in cutting the investigation-and-remediation burden of the incidents you do have, regardless of deploy frequency.

AI is dramatically increasing the speed at which software is created and deployed, but operations has not evolved at the same pace. More code, deployments, infrastructure changes and dependencies mean more potential failure points. The answer is not more observability, telemetry, alerts and people; it is infrastructure with the same intelligence we are putting into software development, systems that understand their state, identify problems and safely take action.

Not necessarily more incidents; the bigger issue is the speed and complexity of modern software environments. The rate of change is starting to exceed what traditional SRE processes can manage manually. Systems are becoming more dynamic and changing faster than humans can operate them.

Their role evolves from operating infrastructure to managing autonomous infrastructure. Instead of scaling by adding people to monitor and respond to every issue, SREs focus on architecture, reliability strategy, security, policies and guardrails.

Less writing every line of code and manually operating infrastructure, and more architecture, system design, security, reliability and higher-level decision-making. AI gives engineers leverage: a smaller team can potentially build and operate systems that previously required much larger teams.

A Digital Immune System for modern infrastructure: an intelligence layer that detects failures, fixes them autonomously, learns from incidents and prevents them from recurring. Infrastructure should move from reactive monitoring to proactive, self-healing operations.