ResolveIQ is best treated as a proposed engineering design: an incident response agent that gets better at investigating a production environment because it stores validated outcomes, not because it accumulates summaries. The workable version is a loop with five parts. It captures the context and outcome of each incident, checks which findings the evidence actually supports, keeps only confirmed lessons with their evidence, retrieves them carefully for later incidents, and uses representative evaluations to confirm that each change helps without making the agent slower, more expensive or less reliable.
The loop matters because a memory store alone is not learning. An agent that saves every postmortem and retrieves similar ones can sound more certain each time while repeating an explanation that was never confirmed.
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What is established about ResolveIQ and its sources
Four different things are easy to conflate here, and they carry different levels of evidence.
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- ResolveIQ is the design described in this article. No source reviewed establishes a launched product by that name, its implementation, or its measured results. Unrelated user-generated projects use similar names, so a search for the name will not confirm anything about this design.
- Resolve AI is a separate commercial AI SRE product. Its product overview and evaluation article are primary sources for what that company says its own platform does. The accessed pages do not state publication dates, so attribute their claims to the company rather than presenting them as independent findings.
- AIOpsLab is a framework described in a Microsoft Research paper. It supports simulation as a way to test operational agents, but it does not show how any commercial agent performs.
- The AIR paper is a 2026 preprint on responding to incidents involving LLM agents. It is emerging work, not settled consensus.
How the system works, step by step
1. Collect context from telemetry and change history
An incident agent is only as useful as the context it can reach. The minimum set is observability data (metrics, logs, traces and alerts) plus change events: deployments, commits, configuration and feature-flag changes, and infrastructure modifications. The first question an on-call engineer asks is “what changed?”, and that question can only be answered if change events are joined to the services they affect.
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Resolve AI’s product overview describes a queryable graph of services, dependencies, deployments and team knowledge, along with integrations spanning code, infrastructure, observability, incident management and CI/CD. The same overview markets “60+” pre-built integrations. That count is a vendor claim and has not been independently verified. ResolveIQ does not need that breadth to begin. Any observability platform that exposes metrics, logs, traces and deploy events can serve as the telemetry source, and nothing here implies a relationship with a named vendor.
Start with two or three services in one environment. Maintain the dependency model and an owner for each service from the outset, because ownership is the route by which team knowledge later enters the system.
2. Investigate through hypotheses tied to evidence
Model each investigation as a set of hypotheses. Each one should carry the claim, the signals that support it, the signals that contradict it, and a status that changes as evidence arrives. An agent that ends with a single paragraph gives the reviewer nothing to check.
Resolve AI’s product description offers one architecture: specialized agents that investigate in parallel, and a verifier that checks conclusions against production evidence. That is the vendor’s description of its own design, not proof that several agents outperform one agent in every case. The part worth adopting regardless of agent count is the separation between proposing a cause and testing it.
- Every hypothesis lists its supporting and contradicting signals with timestamps and query references.
- A hypothesis cannot move to “confirmed” unless a verification step has checked it against production evidence rather than against the agent’s own reasoning.
- Ruled-out hypotheses are kept, because “checked and not the cause” is useful knowledge for the next incident.
3. Record only validated lessons, with an explicit evidence state
This step separates a learning system from an archive. A postmortem’s explanation is a claim, not ground truth. Resolve AI’s evaluation article notes that real incidents may close without an established cause, which makes outcome labels the central constraint on learning. An incident closed with “probably a connection pool issue” and no confirming evidence should not become a rule that future agents apply with confidence.
Give every stored lesson one of the states below. These labels are a proposed scheme for ResolveIQ, not terms taken from any vendor.
| Evidence state | What it means | Can it become a reusable rule? |
|---|---|---|
| Confirmed | Production evidence supports the cause, and the recorded fix was followed by the expected recovery signals | Yes, stored with its evidence links |
| Probable | Evidence points to the cause, but no confirming recovery or recurrence check exists | No; kept as a candidate hypothesis for review |
| Contradicted | Later evidence refutes the explanation | No; kept as a warning that the explanation failed |
| Unresolved | The incident closed without an established cause | No; stores symptoms, timeline and what was ruled out |
Each record should hold more than a summary. At minimum, store the incident identifier, the timeline, evidence references, the resolution as actually performed (not as proposed), the evidence state, the services and dependency path involved, what was ruled out, the date recorded, and the review that set the state. A prose summary cannot be audited later and cannot be relabelled when new evidence arrives.
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4. Retrieve lessons without mistaking similarity for cause
Retrieval should answer “which past lessons might apply here?”, not “what caused this?”. Two incidents can share an alert name and a symptom and still have different causes. Before a retrieved lesson shapes an investigation, the agent should check three things:
- The lesson is in the Confirmed state. Probable lessons can be surfaced as hypotheses to test. Contradicted and Unresolved lessons appear only as warnings.
- The current incident shows the lesson’s preconditions, such as the same dependency path or a matching change event, not merely a similar symptom.
- The lesson has not been superseded. Each lesson carries a valid-from date, and a later incident that contradicts it moves it into review rather than leaving it active.
Resolve AI’s overview describes interactions becoming retrievable context. It does not describe a specific memory algorithm or promise that retrieval improves outcomes, so the retrieval method is an engineering choice to measure, not one to assume. Log which lessons were retrieved, whether the agent cited them, and whether verification confirmed the resulting hypothesis. Those three records are what allow you to say whether reuse helped.
5. Evaluate every change against representative cases
Before any change ships, whether to a prompt, retrieval setting, model or tool, run it against a fixed set of past incidents with known outcomes. Check each case before inclusion. Is the outcome label supported by evidence? Is the incident representative of the services you run? Can the case be solved only by someone who already knows the answer? Calibrate the scorer against expert judgment, because an automated score that disagrees with experienced responders will steer development in the wrong direction.
Resolve AI’s evaluation article states the core constraint directly: “An agent cannot be scored against a conclusion that was never reached.” The article is vendor-authored and does not name individual authors. In practice, correctness scoring should apply only to Confirmed cases. Unresolved cases can still test other behaviour, such as whether the agent surfaces the right symptoms and avoids unsupported claims.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →The same article describes one cost-focused change that increased investigation time by “two to two and a half times.” That is a vendor-reported example rather than an industry statistic, but it illustrates why a release gate must record speed and cost alongside quality. A change can reduce spend while making responders wait.
| Release-gate measure | How to record it | Failure it catches |
|---|---|---|
| Correctness against Confirmed cases | Compare the stated root cause with the labelled outcome, scored by reviewers calibrated to expert judgment | Plausible but wrong conclusions |
| Evidence quality | Share of claims linked to a timestamped signal or query | Unsupported assertions |
| Time to first useful evidence | Elapsed time from invocation to the first signal the reviewer accepts as relevant | Slow starts hidden by good final answers |
| End-to-end latency | Wall-clock time per investigation, reported as p50 and p95 | Regressions in responder wait time |
| Cost per investigation | Model and tool spend per case, recorded for each run | Spend growth from retrieval or parallel agents |
| Lesson reuse effect | Cases where a retrieved lesson was cited and verification confirmed the result, compared with runs without retrieval on the same cases | Memory that adds noise without helping |
6. Test in simulation before production
Microsoft Research’s AIOpsLab paper describes a framework that combines fault injection, workload generation, an agent orchestrator and telemetry observation to simulate incidents and evaluate operational agents. Its stated aim is that “such a framework should enable realistic and reproducible interactions with operational tasks, allowing researchers and practitioners to benchmark their solutions against a common set of criteria” (AIOpsLab paper authors, Microsoft Research).
A simulated environment lets you replay the same fault with the same workload after every change, which production cannot offer. It does not establish that simulated results predict production performance. Use simulation as the first gate. Then run the agent in production in shadow mode, where it writes hypotheses that responders compare with their own conclusions but cannot act on. Only after that comparison should its proposals reach any remediation path.
7. Govern what the agent can do
Start with scoped read access. The agent should query telemetry, change history and the service graph before it can touch anything else. Define which action types require human approval, make every proposed remediation reviewable before execution, and record what happened afterward. Resolve AI’s overview describes configurable autonomy and approval settings for actions. The article cannot verify the exact permission model for a ResolveIQ build, so confirm the controls against the systems you actually run.
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A tiered model keeps these boundaries explicit. The tiers below are a proposal, not a vendor specification.
- Read. Queries against telemetry, change history and the service graph. No approval required.
- Propose. Drafts a remediation with the supporting evidence attached. The on-call engineer reviews it.
- Act. Executes a pre-approved, reversible action, such as rolling back to a known version. Requires explicit approval for each execution, and the outcome is logged.
The motivation for this boundary comes from safety work on agents. The AIR paper, a 2026 preprint, argues that “current safety mechanisms for LLM agents focus almost exclusively on preventing failures in advance, providing limited capabilities for responding to, containing, or recovering from incidents after they inevitably arise.” Because that gap is the one an incident-response agent is meant to fill, the agent’s own actions need containment and rollback paths, not only prevention.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reading vendor performance claims
Resolve AI’s overview displays three performance figures: “72% faster investigation time,” “30% fewer engineers in war rooms” and “100% of alerts investigated.” The accessed page does not give enough context to establish the baseline, sample, incident mix or time period behind any of them. They cannot be compared with other tools, and they should not be read as results a ResolveIQ build would reproduce. If they are useful at all, use them to frame questions about your own baseline: what your current investigation time is for the most common alert types, how many responders typically join a war room, and what share of alerts receive any investigation.
Single-agent or multi-agent: what to measure
The sources describe the rationale for parallel investigation and for multidimensional evaluation, but they contain no independent head-to-head benchmark of single-agent and multi-agent designs. No winner should be declared in advance. Decide with the release gate above, running both designs against the same case set.
| Axis | What to record | Trade-off to watch |
|---|---|---|
| Investigation coverage | Share of signals or hypotheses examined per incident | Parallel work can widen coverage while duplicating queries |
| Time to useful evidence | Elapsed time to the first accepted signal | Coordination overhead can delay the first result |
| Cost and latency | Spend and wall-clock time per case | More agents means more model calls |
| Citation consistency | Whether every claim links to a retrievable signal | Agents may cite differently unless they share one evidence schema |
| Catching wrong hypotheses | Share of incorrect hypotheses that verification rejects | A verifier that simply agrees with the investigator adds cost without value |
Memory approaches deserve the same discipline. Compare retrieval relevance, provenance, handling of contradictory or outdated lessons, and sensitivity to missing ground truth. The sources do not compare named memory implementations, so these axes are suggestions for your own evaluation.
Quick Recap
When the loop underperforms
- Lessons are retrieved but rarely cited. Check whether retrieval is matching on symptom text alone. Low citation usually means the lesson’s preconditions are not being checked.
- The agent confidently repeats an old explanation. Check the lesson’s evidence state and valid-from date. A Probable lesson surfaced as if it were Confirmed, or a superseded lesson still active, is the usual cause.
- Quality scores hold or rise while responders report slower investigations. Compare time to first useful evidence and p95 latency on the same case set. Quality-only scoring hides this pattern.
- Scores move without any code change. Re-check the case set. Labels may have been edited, cases added without review, or the scorer’s calibration may have drifted from reviewer judgment.
- Actions execute without review. Compare the live permission configuration with the intended tiers. An approval setting changed during testing can persist into production.
A practical rollout order
- Build the case set from closed incidents. Label each one Confirmed, Probable, Contradicted or Unresolved, and have reviewers check a sample of labels before any scoring.
- Record a baseline on that case set for the current process, whether manual or an existing tool: correctness, time to first useful evidence, latency and cost.
- Connect read-only context for two or three services. Run the agent in simulation until results are reproducible across repeated runs.
- Run the agent in shadow mode in production, comparing its hypotheses with responders’ conclusions and with the baseline.
- Enable lesson retrieval behind a feature flag, and compare runs with and without it on the same cases.
- Turn on proposed-remediation review first. Enable any action tier only after its audit trail has been checked during real incidents.
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