AI SRE Agent Architecture

Overview

The AI SRE Agent is a cognitive system embedded within your DevOps pipeline. Unlike simple automation scripts, it can observe, reason, act, and learn. This guide explains the architecture that makes this possible.

The agent consists of five primary components:

• Orchestration Layer for workflow management
• Observability Ingestion for signal collection
• Semantic Reasoning Engine for analysis
• Action Chain Engine for remediation
• Feedback Loop for continuous improvement

Orchestration layer

The orchestration layer manages workflow coordination across all subsystems. It handles request routing, parallel task execution, and state management.

# .autohand/sre-orchestrator.yaml
orchestrator:
  mode: async
  max_concurrent_tasks: 10
  timeout: 300s

  pipelines:
    incident_response:
      - ingest
      - analyze
      - propose_action
      - execute_or_escalate

    health_check:
      - collect_metrics
      - evaluate_thresholds
      - alert_if_needed

The orchestrator routes incoming signals to appropriate processing pipelines. It maintains context across multi-step workflows and handles graceful degradation when components fail.

Observability ingestion

The ingestion layer pulls real-time data from multiple sources and normalizes it for analysis:

• Metrics: Pulled from Prometheus, Datadog, or CloudWatch
• Logs: Parsed with NLP using regex fallback and transformer embeddings
• Traces: Converted into causal graphs with time-weighted edges

# Configure ingestion sources
ingestion:
  metrics:
    prometheus:
      endpoint: http://prometheus:9090
      scrape_interval: 30s

  logs:
    sources:
      - type: kubernetes
        namespace: production
      - type: cloudwatch
        log_group: /aws/eks/production

  traces:
    opentelemetry:
      endpoint: http://collector:4317
      sampling_rate: 0.1

Semantic reasoning engine

The reasoning engine combines LLM capabilities with vector search for intelligent analysis:

Vector memory

Past incidents, fixes, and patterns are stored in a vector database. When a similar failure occurs, the agent automatically cross-references and recommends tested resolutions.

# Vector database configuration
memory:
  provider: pinecone  # or weaviate, milvus
  index: sre-incidents
  embedding_model: text-embedding-3-small

  indexing:
    include:
      - incident_reports
      - runbook_executions
      - postmortems
    refresh_interval: 1h

Causal chain generation

The LLM generates structured root cause analysis trees by analyzing correlated signals. It produces natural language summaries alongside technical graphs.

# View reasoning output for an incident
autohand sre explain --incident-id INC-2024-1234

# Output:
# Causal Chain:
# 1. Redis shard 9 memory pressure (95% utilization)
# 2. Connection pool exhaustion (0 available connections)
# 3. API timeout cascade in payment-service
# 4. User-facing 504 errors on checkout
#
# Confidence: 87%
# Similar incidents: INC-2024-0892, INC-2023-1456

Action chain engine

The action chain engine encodes remediation workflows as executable templates. Each action passes through policy validation before execution.

# Action chain definition
action_chains:
  scale_redis:
    steps:
      - name: verify_metrics
        action: check_redis_memory
        threshold: 80%

      - name: scale_out
        action: kubernetes_scale
        resource: redis-cluster
        replicas: "+2"

      - name: verify_recovery
        action: wait_for_metrics
        condition: redis_memory < 70%
        timeout: 5m

    rollback:
      - kubernetes_scale:
          resource: redis-cluster
          replicas: "-2"

Policy enforcement

All actions are validated against Open Policy Agent (OPA) rules before execution:

# Policy configuration
policies:
  engine: opa
  rules_path: /etc/autohand/policies/

  defaults:
    require_dry_run: true
    max_blast_radius: 10%
    allowed_hours: "06:00-22:00"

  overrides:
    critical_incidents:
      allowed_hours: "00:00-23:59"
      require_dry_run: false

Feedback and learning loop

The agent improves through continuous feedback from incident outcomes:

• Postmortem capture: Input from human engineers after incident resolution
• Classification logging: False positives, noise reduction, and success tracking
• Threshold tuning: Bayesian alert scoring adjusts sensitivity over time

# Submit feedback for an incident
autohand sre feedback --incident-id INC-2024-1234 \
  --outcome success \
  --notes "Remediation worked. Consider lowering memory threshold to 75%"

# View learning metrics
autohand sre metrics --period 30d

# Output:
# False positive rate: 12% (down from 23%)
# Mean time to detection: 45s
# Mean time to remediation: 3m 22s
# Auto-resolved incidents: 67%

Integration points

The agent connects to your existing infrastructure through standard interfaces:

Deployment considerations

When deploying the SRE agent, consider these technical requirements:

Memory and reasoning

• Long-term memory via vector databases for incident history
• Prompt chains designed to minimize hallucination
• Semantic compression of log payloads to avoid token limits

Security and guardrails

• All agent actions pass through signed verification
• Role-based access enforced through zero-trust policy engines
• Immutable audit trail stored in secure log vaults