Intent-Based War Room: Natural Language Incident Response
Simply describe your incident in plain English and watch AI orchestrate the entire investigation
An incident has occurred and you're suddenly in investigation mode. You don't yet know if it's the network, the database, a recent deploy, or a downstream dependency misbehaving. But you do know what comes next: jumping between dashboards, tuning queries, collecting logs, and trying to assemble a coherent story from scattered signals.
That setup phase, not the fix, is where most MTTR is lost.
The Intent-Based War Room in HealR removes that friction entirely. You describe the incident in plain English, and HealR orchestrates a complete, multi-agent investigation in under a minute.
Start with a Description, Not a Dashboard
In traditional workflows, you begin with tools: dashboards, logs, queries, filters, and cross-checks. With Intent-Based War Room, you begin with words:
"Users are seeing checkout timeouts in EU after the last deploy."
"Database connections spiked for user-service between 2–3 PM."
"Compare today's CPU usage for cart-service to yesterday."
HealR interprets your description, extracts context, and automatically configures the investigation:
Relevant Services
Automatically identified from your description
Signals
Metrics, logs, and anomalies that matter
Time Windows
Extracted from natural language context
Dependencies
Mapped from your topology
Investigation Objectives
Inferred from your intent
No PromQL. No LogQL. No choosing dashboards. Just describe the situation and the system does the orchestration.
A 5-Stage AI Investigation, End to End
Under the hood, Intent-Based War Room uses the same architecture as the Agentic War Room: a 5-stage, multi-agent pipeline that completes in about 65 seconds.
Metrics Agent
8–10 secondsSurfaces anomalies, latency spikes, error rates, saturation, and unusual patterns. Shows where the system deviated from normal.
Logs Agent
7–9 secondsScans logs for patterns, clusters stack traces, highlights new or rare signatures, and classifies severity. It builds the right queries automatically from your intent.
Correlation Agent
15–19 secondsCombines metrics + logs + topology to reconstruct a timeline: what changed, where it cascaded, and which dependencies were involved.
RCA Agent
7–9 secondsApplies causal inference to propose the most likely root cause, backed by evidence and confidence scoring.
Recommendations Agent
12–15 secondsDelivers prioritized remediation steps, risk analysis, and an executable plan.
By the end, you're not staring at raw data. You're looking at:
- →A structured narrative
- →The likely root cause
- →A ranked list of actions to take next
All triggered from a single sentence.
Natural Language Becomes Your Investigation Console
The Intent-Based War Room turns everyday questions into full investigations:
"What changed in payment-service in the last hour?"
"Find all errors related to database connections since midnight."
"Is increased EU latency correlated with the new deployment?"
HealR knows:
- →Which metrics to pull
- →Which logs matter
- →Which services depend on which
- →How to align everything in time
This lowers the barrier for newer engineers and accelerates senior engineers by eliminating setup work.
Shared Context That Evolves with the Investigation
Every agent writes its findings into a shared state that carries forward through the entire workflow:
Metrics anomalies
Log patterns
Correlation mapping
Topology and blast radius
Root cause hypotheses
Recommendations and plans
Which means follow-ups like:
"What's the regional impact?"
"If we roll back, who benefits first?"
...are answered with full context, not a fresh start.
The investigation becomes a conversation, not a reset loop.
Real, Measurable Impact
The gains come from eliminating repetitive manual steps.
AI as Co-pilot, Not Replacement
Intent-Based War Room doesn't remove humans from the loop. It removes the friction around them.
AI Handles
- →Collects evidence
- →Correlates signals
- →Proposes a root cause
- →Suggests remediation
Humans Handle
- →Judge trade-offs
- →Decide actions
- →Communicate
- →Design long-term fixes
It's the right division of labor.
Ready to Transform Your Incident Response?
See how natural language incident response can reduce MTTR and bring autonomy to your SRE workflows.
Mohammed Parvaiz
Product Owner, AutonomOps AI
Helping engineering teams turn advanced AI-SRE into everyday practice.