Analyze telemetry in place.
Instead of ingesting and indexing terabytes of redundant logs, DataAgent's RCA engine concentrates deep inspection on the small fraction of code paths that determine stability, shrinking expensive log-indexing pipelines.
Autonomous Remediation-first
Accurate context, not queryable gigabytes, is the primary product. DataAgent replaces cached dashboards and pre-computed metric aggregates with live, topology-driven remediation, an agent that pulls high-fidelity signals directly from the source at the exact moment a fault occurs.
MTTR Reduction — Hours to Seconds
Restoring health first and investigating offline is what shortens mean-time-to-resolution
DataAgent reads live system state, topology and configuration drift where the data already sits, diagnoses the fault far enough to restore the system to health fast, and runs the deeper root-cause analysis offline, after service is restored.
- 86%
- By synthesizing logs, dependency maps, and historic commits, DataAgent's predictive models categorize and pinpoint soft bugs and architectural anomalies with 86% precision.
- 1-click
- The moment a bug or vulnerability is classified, DataAgent builds a fully tested, contextually correct remediation script, deployable with one click from the CLI or Git UI.
- 68%
- Automating deployment, scaling, and self-healing configuration reduces manual infrastructure management effort by 68%, freeing SRE teams to focus on architecture instead of firefighting.
Cut Observability Costs by up to 90%
Observability now eats roughly 17% of total infrastructure spend. DataAgent cuts that bill by shifting from reactive, high-volume log ingestion to proactive, topology-based prevention.
End the observability "double bill."
You already pay AWS to produce the data, then pay vendors again to copy, store, and move it. DataAgent reads it natively, in place. No duplication, no egress, no data tax.
Balance cloud resources predictively.
ML-driven load balancing anticipates traffic spikes and scales to real demand instead of fixed thresholds, preventing costly over-provisioning.
Our Approach
Assistant-AI vs. Autonomous Remediation
Summarizing the past vs. fixing the present. Assistant-AI reads yesterday's aggregates; DataAgent reads live topology and acts the instant a fault occurs.
Assistant-AI Model
(Isolated & Linear)Other tools are simply bolting an AI chat interface onto their existing human-shaped data lakes. An LLM reading pre-computed metric aggregates can offer a summary or a suggestion, but it cannot safely execute autonomous remediation in a live production environment.
Writes code at the function level with no view of services, databases, or API contracts.
- Fragile, duplicated code
- Un-indexed database schemas
- No structural awareness
Topology-Based Remediation
(Systemic & Proactive)True agentic architecture relies on live data, signals, and topology-driven analysis rather than centralized caching. Real autonomy requires fetching high-fidelity data on demand, exactly when a fault occurs.
Continuously maps the system's structural graph.
- Auto-verifies cross-repo dependencies
- 86% bug detection precision
- One-click, context-aware hotfixes
How DataAgent Works
The Redesigned Stack
Four layers, read directly from your control planes — from the living model of your system up to trust-gated, self-correcting action.
- Layer 3
Reasoning & Learning.
RCA over topology (SURGE / RIPPLE), trust-gated action (STEER), and verified-outcome learning (WAKE).
- Layer 2
Read-in-Place Sources.
Raw telemetry left at the source, fetched ad-hoc at full fidelity — only for the blast-radius window.
- Layer 1
Thin Edge Detection (PULSE).
Low-data, in-cluster detection emitting a labelled stream to trigger the model.
- Layer 0
Living System Model (TIDE).
The core asset: a versioned graph of entities, config, relationships, and drift history, read directly from control planes.
The Fault Ladder
A discipline of action, not a dashboard improvement
A fault enters at the bottom and graduates up the rungs. If it hasn't earned full autonomy through verified successes, the system automatically routes it to a human for approval.
Try it for free- 01
Detect (PULSE)
Monitors golden signals and anomalies
- 02
Enrich (TIDE)
Topology & drift evidence maps dependencies and config changes
- 03
Diagnose (SURGE)
Provides RCA chain-of-thought
- 04
Gate (STEER)
Applies trust thresholds. Routes to human if unverified
- 05
Act (RAPS)
Executes the verified playbook fix
- 06
Learn (WAKE)
Applies reinforcement learning from verified outcomes
Summary
DataAgent vs. Others
| Dimension | Traditional Development | Assistant-AI | DataAgent |
|---|---|---|---|
| System Context | Human-dependent. Developers must manually memorize or document dependencies. | None — operates on isolated snippets, blind to schemas or APIs. | Living system topology — a real-time, cross-repo knowledge graph. |
| Verification Method | Slow, manual, and inconsistent peer review. | Zero — puts the review burden on humans, a 4.6x PR delay. | Automated originator + QA checksums against live topology. |
| Remediation Capability | Manual refactoring; issues surface post-production. | None — highlights errors, forces manual patching. | One-click auto-remediation with tested, context-aware hotfixes. |
| Technical Debt Prevention | Manual, scheduled refactoring sprints. | High risk — a technical debt machine multiplying copy-paste. | Active de-duplication, blocked at local and PR levels. |
| Observability Profile | Reactive and expensive — ingests massive post-incident log volumes. | Generates bloated code that increases log noise and complexity. | Proactive & lean — prevents bugs pre-merge, focuses on high-leverage paths. |
| Infrastructure & SRE Impact | Siloed teams, days spent on manual scaling. | No integration with DevOps or cloud environments. | 68% less SRE time on routine firefighting — freeing engineers for complex problem-solving and driving better MTTR. |
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