Handles every inbound Tier 1 and Tier 2 support case autonomously — refunds, order status, rescheduling, account updates, troubleshooting — with full live account context from your Salesforce org. Human agents only see what the AI genuinely cannot resolve.
65%+
autonomous resolution
no human required
< 2 min
first response
24/7 coverage
40%
handle time reduction
for escalated cases
98%
CSAT maintained
on AI-resolved cases
Typical build: 3-week sprint · Fixed price · Production-grade
Trigger
Case created
Response time
< 2 min
Resolution
L1 + L2 auto
This is the actual Agentforce configuration Kovil AI builds and deploys — not a diagram. Here is what runs inside every node.
The Agentforce resolution agent fires the moment a new Case record is created — from any channel: web portal, email-to-case, chat, or phone (via voice transcription). The trigger is a Topic mapped to the Case object's creation event. The agent immediately assembles full context: the customer's account history, open and closed case history, recent orders, any active contracts, and their entitlement level. By the time the first action runs, the agent knows the customer better than most human agents reading a case for the first time.
Einstein Case Classification analyses the case subject and description using NLP to identify: case type (refund, order status, account update, technical issue, billing query, rescheduling), urgency tier (L1 routine, L2 complex, L3 escalation required), and sentiment (frustrated, neutral, satisfied). The classification determines which resolution path the Atlas Reasoning Engine takes. L1 and most L2 cases are routed to autonomous resolution. L3 cases — those requiring genuine human judgement — skip directly to escalation with full context pre-loaded.
For each classified case, Atlas reasons through the available actions configured during implementation — the specific resolution capabilities vary by client but typically include: process refund (via Flow calling payment system), check and update order status (via Data Cloud or ERP integration), reschedule appointment or delivery (via calendar system), update account field (directly in Salesforce), retrieve product documentation and troubleshooting steps (via Knowledge Base search), and reset account credentials (via identity system). Atlas selects the correct action, executes it, and verifies the outcome before closing the case.
For troubleshooting and how-to cases, Atlas queries the Salesforce Knowledge Base using semantic search — not keyword matching. The agent retrieves the most relevant articles for the specific product version, customer entitlement level, and issue described. It does not send the article link. It summarises the relevant steps in conversational language, tailored to the customer's apparent technical level based on how they described the problem. If the KB article is outdated or missing, the agent flags the gap for the knowledge management team.
When the resolution action completes successfully, the agent closes the loop: sends a personalised resolution confirmation to the customer via their preferred channel (email, chat, or portal), updates the Case status to Resolved with a detailed resolution summary, logs all actions taken as Case Comments for audit purposes, and triggers a post-resolution CSAT survey via the configured survey tool. Resolution time, action taken, and knowledge article used are all logged for analytics. Cases resolved autonomously are tagged so you can track your autonomous resolution rate over time.
Cases the agent cannot resolve — those requiring genuine human judgement, exceptions outside configured parameters, or customers explicitly requesting human contact — are escalated via OmniChannel to the appropriate human agent queue. The escalation includes: a full AI-prepared case summary (what the customer needs, what was already tried, recommended next action), the customer's sentiment score, their full account context, and any relevant knowledge articles. Human agents receive escalations fully briefed — they do not re-read the case from scratch. Average handle time for escalated cases drops 40% because the AI has already done the diagnostic work.
Resolution reasoning
Reasons through the correct resolution action for each case type — executes the action, verifies the outcome, and closes the case autonomously.
Intent + tier
Classifies every inbound case by type, tier, and sentiment in under a second — determining whether it goes to autonomous resolution or human escalation.
Article retrieval
Provides semantic knowledge search for troubleshooting cases. Agent summarises relevant steps conversationally rather than sending raw article links.
Channel routing
Routes cases from any channel — email, chat, portal, voice — into the resolution agent, and routes escalations to the correct human agent queue.
Live account context
Unifies account history, order data, and contract information so the agent has full customer context before taking any action.
Compliance
Ensures all LLM calls stay within Salesforce's trust boundary. Masks PII, prevents prompt injection, and logs every agent action for compliance audit.
Action execution
Executes the resolution actions — refund processing, order updates, appointment rescheduling — via Flow integrations with your backend systems.
Kovil AI scopes, builds, tests and deploys this Agentforce configuration end-to-end. You do not touch Agent Builder until it is live and resolving cases.
The resolution library is configured during implementation based on your specific service operations. Common autonomous resolutions include: order status checks, refund processing (within configured thresholds), appointment and delivery rescheduling, account field updates (address, contact info, preferences), password and credential resets, subscription changes, and standard troubleshooting for your top 20 issue types. Cases requiring genuine human judgement — complex complaints, exceptions outside policy, legal or compliance-sensitive requests — are always escalated.
Our clients typically achieve 55–75% autonomous resolution rates within 8 weeks of deployment, depending on the breadth of the resolution action library configured. The rate improves over time as the knowledge base is updated and edge cases are added to the agent's action library. We measure resolution rate, CSAT score, and escalation reasons as part of standard post-launch reporting.
Einstein Case Classification includes sentiment detection. Cases flagged as high-frustration trigger a modified resolution path: the agent acknowledges the frustration explicitly before attempting resolution, uses a more empathetic tone in all responses, and has a lower escalation threshold (it escalates sooner rather than attempting multiple autonomous resolution steps). Very high frustration scores can be configured to trigger immediate human escalation.
It handles the high-volume, repetitive L1 and L2 cases that consume the majority of your team's time. Your human agents handle the genuinely complex cases — complaints, exceptions, VIP accounts, strategic situations — fully briefed by the AI. Most clients find this increases human agent satisfaction because they spend their time on meaningful work rather than answering the same questions repeatedly.
Book a 30-minute discovery call. We'll analyse your case type distribution, identify which cases the agent can handle autonomously, and scope a 3-week fixed-price implementation.
3-week sprint · Fixed-price · Production-grade · Post-launch support included