Solutions
Agentic & Autonomous Data Orchestration
LyftData’s agentic framework transforms static pipelines into self-monitoring, self-healing systems that sense context, respond dynamically, and optimize their own performance.
Problem
Traditional orchestration frameworks are reactive. When a schema changes, a volume spike hits, or a downstream system fails, engineers must intervene manually. These static DAGs can’t adapt, causing downtime, lost data, and constant firefighting.
LyftData Solution
- Self-monitoring jobs: workers detect errors and auto-retry using configurable health metrics and thresholds.
- Adaptive routing (experimental): jobs re-route data dynamically based on policy logic or AI model feedback.
- Telemetry feedback loop: performance metrics feed back into orchestration logic for continuous optimization.
- Server insights dashboard: real-time visualization of health states, retry events, and adaptive reroutes.
These features form the foundation of LyftData’s agentic layer — a system that learns from its own telemetry to maintain uptime and efficiency automatically.
Outcome
Pipelines evolve from static flows into living systems that self-correct and optimize. Teams spend less time debugging and more time building data products, while the platform manages the rest.
Example: Self-monitoring job with adaptive routing
A conceptual YAML configuration showing auto-retries, adaptive rerouting, and feedback-driven tuning:
job:
name: adaptive-ingest
monitoring:
health_metrics:
- latency_ms < 5000
- error_rate < 0.01
retry_policy:
attempts: 3
backoff: exponential
source:
type: kafka
topic: transactions
actions:
- parse: json
- enrich: { add_field: timestamp, value: "{{ now() }}" }
- route:
- when: load_avg > 0.8
to: low_priority_sink
- else: high_priority_sink
destinations:
- name: high_priority_sink
type: snowflake
table: live_transactions
- name: low_priority_sink
type: s3
bucket: deferred-transactions
feedback_loop:
enabled: true
metrics:
- latency_ms
- throughput
- error_rate
adjust:
- parameter: batch_size
rule: increase_when latency_ms < 1000
What this does: monitors job latency and error rate, retries automatically, reroutes traffic when system load rises, and tunes batch size dynamically using feedback from the Worker telemetry stream.
Next steps
Explore LyftData’s roadmap for agentic data systems and learn how to enable adaptive orchestration in your own environment.