AI SRE: work the incident.
Dstl8: distill and fix.
Most AI SRE tools start at the alert. One fires, an agent triages it and hands back a root cause, and the answer stays with whoever was on call. Dstl8 is AI SRE for AI-speed engineering, and it closes the loop on both the code and the cluster. Telemetry distillation reads every log line at the source, Möbius finds the failures nobody wrote a rule for, in staging and production, and the cause and a recommended fix land with the coding agent that shipped the change. Here is how Dstl8 compares with Resolve AI, Traversal, Cleric, Datadog Bits and Dash0 Agent0.
# one investigation, either cause 10:02 DSTL8 checkout p95 up 6x, no alert fired 10:03 DSTL8 correlated: deploy #2091 + pod restarts ├─ infra memory limit too low in checkout.yaml │ fix → your agent opens a GitOps PR └─ code retry loop added in PR #2091 evidence + fix → Claude Code / Cursor 10:09 fix merged under your policy. nobody gets paged.
What is an AI SRE?
An AI SRE is software that does site reliability work on its own. It detects and triages problems, investigates across telemetry, infrastructure and code, finds the root cause, and recommends or drives the fix. AI SRE tools come in two kinds, and the difference decides what they can see.
Why AI-speed engineering needs a different AI SRE
Coding agents mean more changes, shipped faster, with failure modes nobody wrote a rule for. Most AI SREs are built on the old structure: collect everything, ship it somewhere central, index it, and wait for an alert. An AI SRE built on that structure inherits its limits.
How do you build trust in an AI SRE?
An AI SRE is only as trustworthy as the telemetry under it. Feed it filtered, sampled or partial data and it still answers, confidently. Garbage in, confident garbage out. Trust has to hold across the whole chain, and the chain starts with the data. The full argument is in How to Trust What Your Agents Ship.
How Dstl8 works as an AI SRE
Four steps, running continuously on every source, on Möbius, our own fine-tuned model reasoning over distilled signal instead of raw logs.
Distill
Telemetry distillation reads the content of every log line at the source and keeps the signal: patterns, anomalies, sentiment and severity. Sentiment scores what a line actually says, so a response that returns 200 with a refusal inside, or a job that reports success and writes nothing, still surfaces. Raw volume stays in your environment; only the signal and selected evidence lines go up.
Correlate
A deploy, a manifest change and a spike in retries read as one incident, tied to the change that caused it, whether that is infrastructure, config or application code.
Reason
Möbius finds the cause on either side of the infra and code line and writes up a recommended fix with the evidence behind it, delivered into Claude Code, Cursor or Codex over MCP.
Remember
Every investigation feeds a knowledge graph, so past incidents and fixes show up before anyone changes a service, and the same cause is not solved twice.
Dstl8 vs Resolve AI, Traversal and Cleric
Three AI SREs built for the incident room. Here is what each one assumes you already have, and what that assumption costs you.
Dstl8 vs Resolve AI
Resolve AI is an incident-response AI SRE built for large SRE organizations. Its agents join your on-call rotation, pick up pages from PagerDuty and your other alerting tools, and reason across the observability stack you already pay for.
| What starts it | Dstl8Nothing you configure. Möbius reads every log line continuously and opens incidents with zero alert rules. | Resolve AIYour alerts, plus background agents watching deployments. Coverage is bounded by the stack and the monitors you already maintain. |
|---|---|---|
| Where the data comes from | Dstl8Its own. Dstl8 distills logs at the source from Kubernetes, AWS, OpenTelemetry, Vercel, Supabase and Railway, so no observability stack is required. | Resolve AIWhatever your existing observability, infrastructure and code tools already collected. Sampled or never-instrumented services stay invisible. |
| Who acts on the answer | Dstl8Your own coding agent, with the cause, the evidence and a recommended fix. Your own agent applies it under your policy: fully automated, or reviewed first. Dstl8 never touches production. | Resolve AIResolve’s own agents, acting on your production systems within guardrails you have to define and maintain. |
| Pricing and setup | Dstl8Published plans from $50/mo, not metered by investigations or incidents. Self-serve trial; connect a source in minutes. | Resolve AIOutcome-based pricing through sales. You learn the price after the demo, and it is tied to how much work the agents do. |
The catch. Resolve is built to make a large on-call team faster. Without a mature observability stack, alerting and a rotation to plug into, there is little for it to work with, and its agents, not yours, are the ones acting in production.
Dstl8 vs Traversal
Traversal is an enterprise AI SRE built for the largest production estates. It indexes the telemetry you already collect into a causal model, then searches it for the root cause of incidents your alerts surface.
| What starts it | Dstl8Nothing you configure. Möbius reads every log line continuously and opens incidents with zero alert rules. | TraversalYour alert streams, triaged continuously, plus background workers. The model is built around incidents in production. |
|---|---|---|
| Where the data comes from | Dstl8Its own. Dstl8 distills logs at the source, including platforms most tools skip: Vercel, Supabase and Railway. | TraversalTelemetry you already collect and pay to store, read from your existing tools. If it was never instrumented or never shipped, it is not in the model. |
| Who gets the answer | Dstl8Your coding agent over MCP, with the cause, the evidence and a recommended fix. Your own agent applies it under your policy: fully automated, or reviewed first. Dstl8 never touches production. | TraversalThe incident team, with a root cause and guided remediation. The coding agent that made the change is not where the answer lands. |
| Pricing and setup | Dstl8Published plans from $50/mo. Self-serve trial; connect a source in minutes. | TraversalSales-led enterprise deployment. No published pricing and no self-serve trial. |
The catch. Traversal makes the most sense when you already collect and store everything and staff an SRE organization to act on the answer. For a platform team shipping with coding agents, that is a lot of machinery between a bad change and the fix.
Dstl8 vs Cleric
Cleric is an AI SRE that follows each change after merge and checks it under production traffic, using the observability tools you already run.
| When a change gets checked | Dstl8In staging and test as well as production, plus continuous detection with zero alert rules. The verdict arrives before customers see the change. | ClericAfter merge, under real production traffic. Your customers are the test environment. |
|---|---|---|
| Where the data comes from | Dstl8Its own. Dstl8 distills logs at the source, so no observability stack is required. | ClericYour existing observability, CI/CD and incident tools, such as Datadog and Grafana. It inherits their gaps and their bills. |
| Where the answer goes | Dstl8Your coding agent over MCP, with the cause, the evidence and a recommended fix. Your own agent applies it under your policy: fully automated, or reviewed first. Dstl8 never touches production. | ClericFindings in Slack with links to evidence, then fixes handed off. The loop runs through chat, not the coding session. |
| Pricing and setup | Dstl8Published plans from $50/mo, not metered by investigations or incidents. Self-serve trial. | ClericPricing not published. Talk to sales. |
The catch. Cleric is closest to Dstl8 in intent, but it checks changes where mistakes cost the most: in production, on top of a stack you already pay for. Dstl8 gives every change a verdict in staging first, from data it distills itself.
Dstl8 vs Datadog Bits Investigation and Dash0 Agent0
If your telemetry already lives in Datadog or Dash0, their built-in AI SRE is the first thing you will evaluate. The question is not whether to keep the platform; it is which AI does the detecting.
AI SRE capability comparison
Grouped by kind, because capabilities vary vendor to vendor within each group. A dot means some vendors in the group do it, or do it partly.
| Capability | Dstl8 | Standalone | Built-in |
|---|---|---|---|
| Distills telemetry at the source, before it is stored | Supported | Not supported | Not supported |
| Measures sentiment on every log line | Supported | Not supported | Not supported |
| Detects failures with zero alert rules written | Supported | Partial or varies by vendor | Partial or varies by vendor |
| Brings its own data; no observability stack required | Supported | Not supported | Not supported |
| Runs in staging and test as well as production | Supported | Partial or varies by vendor | Partial or varies by vendor |
| Finds the cause in infrastructure, config or code | Supported | Supported | Supported |
| Delivers into Claude Code, Cursor or Codex | Supported | Partial or varies by vendor | Partial or varies by vendor |
| Your own agent applies the fix, under your policy | Supported | Partial or varies by vendor | Partial or varies by vendor |
| Learns from past incidents | Supported | Supported | Supported |
| Published, self-serve pricing | Supported | Not supported | Supported |
| Not metered per investigation or AI credit | Supported | Partial or varies by vendor | Not supported |
✓ Supported ● Some vendors, or partly — Not supported · Standalone: Resolve AI, Traversal, Cleric · Built-in: Datadog Bits Investigation, Dash0 Agent0
AI SRE pricing: metered investigations vs included incidents
Metered investigations change behavior: teams save them for the alerts that matter most, so minor incidents and quiet regressions never get looked at. Dstl8 does not meter investigations or incidents, so every one gets investigated.
| Tool | How the AI is priced |
|---|---|
| Dstl8 | Published plans: Pro from $50/mo and Scale from $250/mo, priced on sources, users, agents and retention. Not metered by investigations or incidents. |
| Datadog Bits Investigation | Usage-based AI pricing on top of your Datadog bill, metered per investigation. |
| Dash0 Agent0 | $0.60 per Agent0 credit, charged by what each run consumes at the model level you choose. |
| Resolve AI | Outcome-based pricing, through sales. |
| Traversal | Not published; through sales. |
| Cleric | Not published; through sales. |
Based on public materials as of October 2026. See Dstl8 pricing for plan details.
Dstl8 or another AI SRE: which to pick
Two different problems. Here is how to tell which one you have.
Pick Dstl8 if
- Platform team: yours runs the infra, stands up the coding agents and gets paged when agent-written code hits production
- Unknowns: your failures are quiet regressions and unknown-unknowns no alert rule would catch
- Delivery: you want the fix to reach the coding agent, and you want to decide how much of the loop runs on its own
- Any stack: you run a mature observability stack or none at all, across Kubernetes, AWS and PaaS like Vercel, Supabase or Railway. Dstl8 distills at the source either way
- No setup tax: you don’t want to write alert rules or build a rotation before an AI SRE can help
- Cost: you want every incident investigated, not just the ones worth a metered run
An incident-first AI SRE makes sense if
- Budget: you have a six-figure budget and an enterprise sales cycle to spend it on
- Onboarding: you’re happy to pay for forward-deployed engineers to get it running
- Double bill: you’re fine paying twice: once for your observability platform, and again for an AI SRE to read it
- Velocity: your AI coding volume is flat and isn’t about to grow
- Telemetry: your telemetry volume and bill are under control, now and next year
- Production is the test: finding problems after they reach customers is acceptable for your business
What Dstl8 leaves to the tools you already have
Dstl8 does one job: find the cause and get the fix to the right place. These stay where they are.
Questions people ask about AI SRE tools
Short answers, written to stand on their own.
What is an AI SRE?
An AI SRE is software that does site reliability work on its own: it detects and triages problems, investigates across telemetry, infrastructure and code, finds the root cause, and recommends or drives the fix. Some AI SREs are standalone products that reason over the tools you already run; others are built into an observability platform and reason over the data that platform stores.
Is Dstl8 an AI SRE?
Yes. Dstl8 is AI SRE tooling that closes the loop for both infrastructure and code, and feeds what it finds back into engineering rather than stopping at the incident. It distills telemetry at the source, finds incidents with no alert rules, and hands the cause and a recommended fix to your coding agent, which applies it under your policy, fully automated or reviewed.
What is telemetry distillation?
Telemetry distillation reads telemetry at the source, before it is stored, and keeps only the signal: the patterns, anomalies, sentiment and severity in every log line. Raw volume stays where it was produced; only the signal and selected evidence lines move on. Dstl8 is built on telemetry distillation, which is why it needs no observability stack and does not meter investigations.
What is log sentiment?
Log sentiment scores what a log line says, not just its level or status code. A response that returns 200 with a refusal inside, a retry storm logged at INFO, or a job that reports success while writing nothing all carry negative sentiment. Dstl8 scores sentiment on every line, so failures that never raise an error or trip an alert still surface.
What is telemetry trust?
Telemetry trust is confidence that the data your dashboards, alerts and AI agents rely on is complete enough to support their conclusions. Most teams cut cost by filtering, sampling and choosing what to index, so everything downstream sees only what survived those choices. Dstl8 reads every log line at the source before anything is dropped, so its conclusions rest on the full stream.
How do you build trust in an AI SRE?
Start with the data: an AI SRE reasoning over filtered or sampled telemetry can be confidently wrong. Then require evidence for every conclusion, keep actions under your own permissions and review, and keep a record of what the AI found and changed. Dstl8 is built that way: it distills every log line at the source, cites the evidence behind each finding, hands the fix to your own agent under your policy, and records every investigation. How to Trust What Your Agents Ship covers the full chain.
How is Dstl8 different from Resolve AI?
Resolve AI is built to speed up a large on-call team: it starts from your alerts, reasons over the observability stack you already pay for, and its own agents act on production within guardrails, priced on outcomes through sales. Dstl8 brings its own distilled data with no observability stack required, detects with zero alert rules in staging as well as production, and hands the fix to your own coding agent, on published self-serve pricing.
How is Dstl8 different from Traversal?
Traversal is a sales-led enterprise AI SRE that reasons over telemetry you already collect and pay to store, and hands the root cause to the incident team. Dstl8 is built for platform teams shipping with coding agents: it distills its own data, including Vercel, Supabase and Railway, finds failures with no alert rules, and delivers the cause and a recommended fix into Claude Code, Cursor or Codex, with a self-serve trial and published pricing.
How is Dstl8 different from Cleric?
Both follow what a change did after it shipped. Cleric checks changes under real production traffic, on top of your existing observability tools, and reports in Slack. Dstl8 gives every change a verdict in staging and test before customers see it, distills its own data, and hands the fix to your own coding agent under your policy.
How is Dstl8 different from Datadog Bits or Dash0 Agent0?
Bits Investigation and Agent0 are AI SREs built into their observability platforms. They reason over what the platform stores and are metered per investigation or per credit. Dstl8 reads every log line at the source before anything is stored or dropped, opens incidents nobody wrote a rule for, and is not metered by investigations or incidents.
Does Dstl8 make changes in production?
No. Dstl8 reads telemetry and hands over a cause, evidence and a recommended fix. Your own agent applies it, under your permissions and your review process. Some teams let that run fully automated; others require approval on every change. For GitOps teams, your agent opens the fix as a pull request to the repo of record.
How are AI SRE tools priced?
It varies. Platform-built AI SREs are usually metered per investigation or per AI credit on top of the platform bill. Standalone AI SREs are usually priced through sales, sometimes on outcomes. Dstl8 publishes its plans, from $50/mo, and does not meter investigations or incidents, so minor incidents and quiet regressions get investigated too.
Do I need an SRE team to use Dstl8?
No. Dstl8 works for teams with no SRE function, no alert routing and no on-call rotation, and it works alongside one.
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Resolve AI, Traversal, Cleric, Datadog, Bits AI and Dash0 are trademarks of their respective owners. ControlTheory is not affiliated with or endorsed by any of them. What we describe here comes from each vendor’s public materials and was correct in October 2026. Capabilities and pricing change quickly in this category, so check anything decision-critical with the vendor.
Sources: Resolve AI: AI SRE · Resolve AI pricing model (VentureBeat) · Traversal · Traversal Causal Indexer · Cleric · Bits Investigation docs · Agent0 credits · Dstl8 pricing
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