Capabilities

Autoheal's AI agents are purpose-built for production engineering. This page covers the key capabilities that differentiate Autoheal from generic AI tools.

Alert Normalization & Deduplication

When an incident occurs, it typically generates multiple alerts across different tools — a PagerDuty page, a Datadog monitor trigger, a Grafana alert, and a spike in Sentry errors. Instead of creating separate investigations for each, Autoheal:

• Normalizes alerts from different sources into a consistent format with standardized severity, timing, and service attribution
• Deduplicates alerts that refer to the same underlying issue
• Categorizes by service, failure domain, and probable impact
• Correlates simultaneous alerts across services to identify cascading failures vs. independent issues

The result: your on-call engineer sees one consolidated investigation instead of a wall of noise.

Decision Traces

Every investigation decision is documented in a decision trace — a transparent record of the agent's reasoning.

A decision trace captures:

Element	Description
Data sources queried	Which tools were consulted and what data was retrieved
Hypotheses considered	All probable causes the agent evaluated, not just the final answer
Evidence for/against	What data supported or contradicted each hypothesis
Path decisions	Why the agent pursued certain diagnostic paths over others
Confidence levels	How confident the agent is in each finding and why

Why Decision Traces Matter

• Auditability — your team can review exactly how the agent reached its conclusions
• Trust — engineers can verify the reasoning, not just the result
• Learning — when the agent is wrong, decision traces show where the reasoning went off-track
• Compliance — provides the documentation trail that regulated environments require

Adversarial Agent Review

Before presenting findings to your team, Autoheal runs an adversarial review process. A separate validation step challenges the primary investigation by:

• Evidence checking — verifying that every finding is backed by concrete, retrievable evidence
• Gap identification — flagging areas the investigation didn't cover that it should have
• Logic validation — ensuring the causal chain from symptoms to root cause is logically sound
• False positive filtering — catching correlations that don't imply causation

This adversarial approach significantly reduces false positives and ensures that when Autoheal presents a root cause, your team can act on it with confidence.

Multi-Turn Conversations

Autoheal investigations are interactive, not fire-and-forget. The OnCall Agent maintains full conversation context, enabling:

• Follow-up questions — "Can you also check the Redis connection pool metrics?"
• Investigation redirection — "I think this is related to the database migration we ran at 3 PM"
• Clarification requests — "What exactly changed in that deployment?"
• Scope expansion — "Are any other services seeing similar issues?"

The agent remembers everything discussed in the investigation and incorporates new directions without losing prior context.

Preventive Fixes

When the root cause is a code defect, Autoheal goes beyond identification to show your team exactly what to fix:

1

Root Cause Identification

The agent pinpoints the specific file, function, and logic causing the issue, linking it to the evidence from the investigation.

2

Fix Proposal

Based on the root cause analysis, the agent generates a proposed code change that addresses the underlying defect.

3

Team Review

Your engineering team reviews the suggested fix with full context on why the change was proposed and how it connects to the incident.

This surfaces actionable fixes directly from the investigation, so your team knows exactly what to change to prevent recurrence.

5-Why Root Cause Analysis

Autoheal generates structured RCAs using the 5-Why methodology — iteratively asking "why?" until the underlying systemic cause is identified, not just the proximate trigger.

A typical 5-Why RCA includes:

• Incident summary — what happened, when, and the business impact
• Timeline — chronological events built from investigation evidence, not from memory
• 5-Why chain — iterative root cause analysis from symptom to systemic cause
• Impact assessment — services affected, duration, user impact, SLA implications
• Mitigations applied — what was done to resolve the immediate issue
• Preventive measures — action items to prevent recurrence, with owners and timelines

Guaranteed Post-Mortems

Because the RCA is generated from actual investigation evidence, every incident gets a thorough post-mortem — not just the ones someone remembers to write up. This eliminates the common failure mode where only major incidents get RCAs while smaller issues quietly recur.

Skill Execution

The agent doesn't just find problems — it follows your team's established procedures to fix them. When the Production Context Graph contains relevant skills, the agent:

• Matches the current issue to applicable skills based on symptoms and service
• Follows documented remediation steps, adapting to the specific situation
• Reports which steps were taken and their outcomes
• Suggests skill improvements when steps are missing, outdated, or unclear

Knowledge Evolution

Every investigation enriches the Production Context Graph:

• New connections discovered between services, failure modes, and resolutions
• Successful diagnostic paths are reinforced for similar future issues
• Dead-end paths are deprioritized to save time in future investigations
• Resolution patterns become reusable across similar incidents
• Skill gaps identified during investigations prompt Production Context Graph improvements

This creates a continuous improvement cycle where institutional knowledge compounds over time instead of being lost to turnover and context-switching.