Krista executes end-to-end business processes as a unified, cognitive system.

How the Krista Email Response Agent Works

Scroll down to explore each stage of the workflow. The panel on the right updates as you progress through each step.

STEP 1 OF 6

Monitor Inboxes

Krista connects directly to shared inboxes like support@ or info@. Krista monitors incoming mail streams 24/7 and maintains a durable state, ensuring no process "times out" during long-running inquiries.

STEP 2 OF 6

Detect Intent via Native ML

Krista identifies the topic, urgency, and sentiment of every email. She uses native machine learning models built on your data to ensure higher accuracy than generic prompts.

STEP 3 OF 6

Retrieve Enterprise Data

Krista performs autonomous lookups in CRM, ERP, or ticketing systems. She uses a constructed memory to ensure that every agent on the platform understands the current status of the customer's account.

STEP 4 OF 6

Send Response or Execute Action

The platform drafts or sends the reply using your approved templates. This ensures the tone is perfect and the solution is correct based on real-time enterprise data.

STEP 5 OF 6

Governed Human-in-the-Loop Escalation

When Krista needs help she routes issues or asks for help for edge cases or when the machine learning model doesn’t confidently know the answer to the correct human expert. She provides a summarized context and quantifies her uncertainty so your team can make informed decisions.

STEP 6 OF 6

Log, Close, and Learn

Krista updates the ticketing system and logs the interaction for analytics. Every resolved case feeds back into the platform's memory, making the system smarter over time.

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Get Started with Krista

That's the full Krista workflow in action. From monitoring inboxes to governed human-in-the-loop escalation and continuous learning, Krista handles it all with speed, accuracy, and brand safety.

Krista Agent
New email arrived in support@yourcompany.com
From: customer123@example.com
Subject: Where is my order #ORD-45678?
Monitoring shared inbox 24/7... New message detected. Establishing durable connection state.
Step 1 complete. Moving to intent detection.
Krista Agent
Reading email content → subject, body, attachments... Parsing natural language input. Extracting key entities: order number ORD-45678 Intent keywords: ("where is", "order").
Krista AI Intent Detection Illustration
Calling native ML intent classifier (trained on your historical data)... Analyzing topic, urgency, and sentiment. Model confidence: 94%.
Intent detected: Topic → Order status inquiry
Urgency → Medium
Sentiment → Neutral / slightly anxious
Primary intent label → "shipment_question / track_order"
Building shared memory context... Customer email matches existing record. Preparing for autonomous data lookup in next step (CRM / ticketing system).
Step 2 complete. High-confidence context established. Initiating autonomous enterprise data retrieval.
Krista Agent
Salesforce CRM Oracle ERP Zendesk Ticketing
Retrieving real-time data from connected enterprise systems... Connected: Salesforce CRM, Oracle ERP, Zendesk ticketing.
Found matching customer record in Salesforce.
Order status pulled from ERP: Shipped via UPS.
No open tickets in Zendesk – proceeding to draft personalized response.
Step 3 complete. All required data retrieved with high confidence. Moving to response generation.
Krista Agent
Collecting all relevant information and data... Merging real-time results from Salesforce CRM, Oracle ERP, Zendesk ticketing, and shared memory context.
Customer record confirmed: • Order #ORD-45678 status: Shipped • ETA: 2 days • No open tickets • Sentiment: Neutral/slightly anxious.
Retrieving approved brand template... Matching intent label "shipment_question / track_order" to your pre-approved response library.
Selected template: "Order-Status-Update-Standard-v3" (contains placeholders for order number, carrier, ETA, and empathetic tone).
Building structured prompt for Krista LLM... Injecting live data into template slots • Enforcing exact brand tone guidelines • Adding personalization rules (customer name, previous interaction history) • Including compliance & accuracy guardrails.
Calling Krista LLM (fine-tuned on your historical data & tone)... Generating response with high fidelity.
Model confidence: 98%.
Step 4 complete. Personalized reply drafted and and delivered.
Krista Agent
Evaluating response confidence before send... Intent classification: 94% (from Step 2)
Data retrieval completeness: 100%
Generated response personalization & tone match: 72%
Overall confidence score: 78%

Threshold not met (below 85%). Triggering governed human-in-the-loop escalation.
Routing to human expert... Detected edge case: Customer mentioned "damaged package" in follow-up sentence not captured in primary intent.
Uncertainty source: Ambiguous damage description + no matching photos or prior claim in Zendesk.
Recommended assignee: Customer Support Tier 2 (SME: Shipping & Claims)
Human escalation ticket created & assigned Sending summary context to agent dashboard...
Waiting for human input... Escalation complete. Krista paused — will resume once agent provides direction or approval.
This ensures 100% accuracy and brand safety on uncertain cases.
Step 5 complete (conditional). Governed human-in-the-loop activated for edge case handling. Process remains fully auditable and improvable.
Krista Agent
Finalizing interaction – entering logging & learning phase... All steps completed. Response delivered (or escalated & resolved with human input).
Updating ticketing system for compliance & audit trail... Logging full interaction metadata to Zendesk.
• Ticket #SUP-98765 updated & closed
• Full email thread archived
• Compliance tags applied (e.g., GDPR/PII handled, no sensitive data exposed)
Feeding resolved case back into Krista platform memory... Preparing anonymized learning dataset.
Learning loop complete. Interaction logged for analytics & compliance.
Model improved incrementally.
Krista is now smarter for future inquiries from this customer and similar ones.
Step 6 complete – cycle finished. Ready for next incoming email. Continuous improvement active 24/7.
Krista Agent
That's the full Krista workflow in action.

The result?
Fast, accurate, on-brand responses to routine questions — with zero risk on edge cases thanks to governed human-in-the-loop and continuous learning.

This is what happens automatically for every email when you use Krista.
Building this yourself would require:

• Custom machine learning models trained on your data
• Enterprise-grade security & compliance (PII handling, audit trails, GDPR/SOX)
• Robust governance & confidence scoring
• Real-time integrations to every system (CRM, ERP, ticketing)
• A safe way to detect out-of-bounds conditions and seamlessly engage the right human expert
• Ongoing reinforcement learning from every resolved case

That's a massive engineering lift — and most teams never finish it.
Let's talk about how Krista can transform your shared inbox today.