Detects case complexity, SLA breach risk, and customer sentiment in real time. Routes every escalation to the correct human agent with full context pre-loaded — no re-explanation, no case re-reading, no dropped context. Your agents start every escalation already briefed.
40%
handle time reduction
on escalated cases
< 30s
context pre-loading
before agent sees case
95%
first-contact resolution
on escalations
Zero
re-explanation needed
from customers
Typical build: 3-week sprint · Fixed price · Production-grade
Trigger
Case created
Context load
< 30 seconds
Routing
Skill + SLA + sentiment
This is the actual Agentforce configuration Kovil AI builds and deploys — not a diagram. Here is what runs inside every node.
The Intelligent Escalation agent fires on case creation and on case status changes — not just at initial intake. This means it can re-evaluate routing mid-lifecycle as cases develop. At intake, it immediately fetches: full customer account and history, entitlement level and SLA terms, current case queue loads (so it can route to the least-loaded appropriate queue), and any previous cases with the same root cause. The triage happens in background — from the customer's perspective, they submitted a case; from the agent's perspective, the case arrives pre-sorted.
The Atlas Reasoning Engine analyses 12+ signals to produce a complexity score for each case: case description length and technical vocabulary, product version and known issue cross-reference, number of components involved, regulatory or compliance implications (if the case touches financial data, health data, or contractual obligations), customer account tier, similar resolved case outcome patterns, and whether the case type has a known autonomous resolution path. Complexity scores above threshold route to specialist queues; below threshold route to general queues or autonomous resolution.
For each case, the agent calculates SLA breach risk based on: the customer's contracted SLA tier, time elapsed since case creation, current queue depth for the appropriate specialist, and historical handle time for this case type. Cases approaching breach threshold are automatically elevated in the queue — they skip ahead of lower-priority cases regardless of creation timestamp. Cases already in breach trigger an immediate Slack alert to the team lead with the case details and recommended action. SLA compliance rate is tracked as a live dashboard metric.
Einstein analyses the case description and any prior interaction history for sentiment signals. Sentiment scores three dimensions: frustration level (is the customer upset?), urgency (how time-sensitive is this for them?), and churn risk (based on account history and issue severity, how likely are they to churn if this is not resolved well?). High-frustration or high-churn-risk cases are routed to senior agents, not the general queue. The pre-loading context note for the assigned agent includes the sentiment score and a suggested opening tone — the agent knows to lead with empathy before troubleshooting.
The routing decision uses OmniChannel with custom routing logic configured during implementation. The agent considers: specialist skill tags (which agents are certified for which product areas), current availability and queue load (to prevent any one agent being overloaded), the customer's preferred language (routes to language-matched agents where available), account ownership (routes to the account-owning rep's support pod where configured), and historical success rate (which agent types have the highest CSAT for this case type). The routing decision is logged on the case record with reasoning.
The moment a case is assigned, the agent generates and attaches an AI-prepared context brief to the case record. The brief includes: a 3-sentence case summary (what the customer needs, what was already tried, what the recommended action is), the customer's sentiment score and suggested opening, full account history highlights (relevant previous cases, purchase history, contract details), a recommended resolution path based on similar resolved cases, and any knowledge articles relevant to the likely root cause. The human agent opens a case already knowing everything they need. Average handle time drops 40% because there is no case re-reading or context reconstruction.
Complexity scoring
Analyses 12+ signals to score case complexity and determine the correct routing destination and agent tier.
Intent detection
Classifies case type and identifies regulatory flags, known issues, and autonomous resolution eligibility at intake.
Skill-based routing
Routes cases to the correct agent based on skill tags, availability, queue load, language match, and account ownership rules.
Account context
Unifies account history, purchase data, and prior case outcomes to power the AI context brief and churn risk scoring.
Compliance
All customer data remains within Salesforce's trust boundary. No PII leaves the platform during AI processing.
Breach + VIP alerts
Sends real-time alerts to team leads for SLA breach risk and VIP customer escalations — with full context and recommended action.
Queue management
Executes queue reprioritisation, SLA escalation flows, and context brief generation as automated background processes.
Kovil AI scopes, builds, tests and deploys this Agentforce configuration end-to-end. You do not touch Agent Builder until every escalation arrives pre-briefed.
OmniChannel routing uses skill tags configured on each agent profile. During implementation, we work with you to define your skill taxonomy (product areas, languages, customer tiers, specialist certifications) and tag your agents accordingly. The routing engine then matches case requirements to agent skills, filtered by current availability and queue load. Account ownership routing (routing to the pod that owns the account) can also be configured.
Yes. Re-escalation triggers a new routing decision with updated context — the complexity score increases (it is now a case that failed first-contact resolution), the sentiment score is re-evaluated, and the new AI context brief includes what the first agent attempted and why it did not resolve. The second agent receives a fully updated brief, not a cold case.
The agent continuously monitors all open cases against their SLA timers. When a case reaches 75% of its SLA window without resolution, it is automatically elevated in the queue. At 90%, a Slack alert fires to the team lead. At 100% (breach), a manager alert fires. These thresholds are configurable. The queue reprioritisation happens in real time — agents see their queue re-sort as SLA risk changes throughout the day.
The brief supplements, not replaces. The agent still has access to the full case record. The brief gives them a 3-sentence head start — what the customer needs, what was tried, what to do next — so they spend 30 seconds getting oriented rather than 5 minutes reading through case history. This is where the 40% handle time reduction comes from.
Book a 30-minute discovery call. We'll map your current escalation paths, identify where context is being lost, and scope a 3-week Agentforce implementation that routes every case to the right agent — already briefed.
3-week sprint · Fixed-price · Production-grade · Post-launch support included