From Context to Action Workflow
AI agents do more than execute—they infer, adapt, and optimize. With cognitive memory, agents reason across timelines and act with precision.
Contextual Memory. Build For Autonomy.
Meet AI agents that remember with context, not just code. From surfacing answers through conversation to resolving issues across teams, they think fast and work collaboratively.
Gives the Power to Predict and Prevent
From escalations to synchronizations — powered by ADM agents
Agents Without Contextual Memory
Diagnose same pipeline issue repeatedly
Manual prompt tuning each release
Escalations from broken context
Agents act statelessly, needing constant supervision
Agents With Contextual Memory
Agent recalls patterns and fixes instantly with oversight
Agent adapts dynamically based on past feedback
Agents synchronize context by persona and resolve early
Agents reason with context with human-in-a-loop
Agents Who Know What Happened Last Time
When a familiar ETL issue arises, ADM agents tap into contextual memory for resolution
Incident
Pattern Recognition
Recommendation
Optional Review
Resolution
Recall previous anomalies and their fixes
Match current behavior to past incidents.
Conversational follow-up to confirm or escalate actions across teams.
Recommend or autonomously apply a fix
Resolve the issue faster—no escalations required
Agents Who Know What Happened Last Time
When a familiar ETL issue arises, ADM agents tap into contextual memory:
Incident:
Recall previous anomalies and their fixes.
Recall previous anomalies and their fixes.
Pattern Recognition:
Match current behavior to past incidents.
Match current behavior to past incidents.
Recommendation:
Conversational follow-up to confirm or escalate actions across teams.
Conversational follow-up to confirm or escalate actions across teams.
Optional Review:
Recommend or autonomously apply a fix.
Recommend or autonomously apply a fix.
Resolution:
Resolve the issue faster—no escalations required.
Resolve the issue faster—no escalations required.
Log
Agents capture interactions, metrics, anomalies, and outcomes—building operational memory.
Structure
xLake organizes memory into semantic, procedural, and episodic layers.
Infer
Agents detect patterns, infer causality, adjust reasoning complexity, and prioritize actions based on historical signals.
Actuate
Agents trigger workflows, recommend resolutions, or self-correct—aligning with business intent and operational context.
Evolve
Agents learn from user interactions, update knowledge bases, and support collaboration across multiple users
Example: Eliminate Recurring ETL Failures
3x faster resolution
90% fewer escalations
100% continuity across workflows
One global bank used memory-enabled agents to eliminate recurring ETL failures. Agents identified a past issue, recalled the fix, and applied it before the problem escalated. The engineering team validated and moved on.
Scan
Data Assets
Validate
Quality Metrics
Tag
Metadata
Verify
Regulations
Alert
On Issues
Report
Compliance
Enterprise Outcomes, Realized
"By leveraging historical data and contextual insights, we've streamlined our claims processing, reducing resolution times by 30% and enhancing customer satisfaction."
— Head of Data Automation, Global Insurance Firm
— Head of Data Automation, Global Insurance Firm
Supercharge Your Data with ADM
Contextual Memory powers ADM with dynamic context. Explore all capabilities
Got Questions? Get Clarity
Q1: What is contextual memory in data operations?
A built-in agent capability to recall past data, recognize patterns, and act intelligently across workflows.
Q2: How does it compare to traditional observability or metadata tools?
Unlike static tools, memory lets agents connect past outcomes to present decisions—enabling context-aware actions, not just monitoring.
Q3: How is contextual memory implemented in xLake?
xLake links memory types using embeddings, temporal graphs, and transformers to enable agent reasoning across time series.
Q4: How does contextual memory support privacy and governance?
Agents enforce policies, explain decisions, and ensure auditability—powered by memory and surfaced in the Business Notebook, all with human-in-a-loop.
Q5: Can agents learn and improve autonomously?
Yes, agents learn from outcomes, adapt logic, and evolve without retraining—driven by contextual memory.
Q6: Who benefits from contextual memory?
Data, AI, compliance, and leadership teams needing trusted, proactive, and explainable data operations benefit directly.
Q7: How does contextual memory manage risks like sensitive data exposure?
Agents enforce privacy guardrails during both inference and memory stages, ensuring sensitive information remains protected even with expanded context windows.
Q8: Can agents take action directly or only suggest fixes?
Agents can suggest, execute, or offer options—depending on trust level and configured guardrails.
Q9: Do agents support chat or conversation?
Yes, agents respond conversationally across use cases, adapting to user role and previous context.
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