Monitors every active campaign in Marketing Cloud around the clock. Detects underperformance before it becomes a problem, adjusts segment targeting based on live engagement signals, pauses journeys that are hurting deliverability, and alerts your team to decisions that need human judgement — so your campaigns never run unattended.
24/7
campaign monitoring
no weekly review lag
30%
engagement lift
from dynamic targeting
< 1 hr
response to signals
vs next-day review
2×
campaign ROI
on optimised journeys
Typical build: 3-week sprint · Fixed price · Production-grade
Signals monitored
Continuous
Response time
< 1 hr
Optimisation
Autonomous
This is the actual Agentforce configuration Kovil AI builds and deploys — not a diagram. Here is what runs inside every node.
The Campaign Execution Agent connects to Marketing Cloud's engagement data stream via Data Cloud, ingesting real-time performance signals across every active journey: open rates, click-through rates, unsubscribe rates, conversion events, and send-time engagement patterns. Signals are evaluated against your configured benchmark thresholds — not industry averages, but your own historical campaign performance as the baseline. The agent monitors all campaigns simultaneously, not just flagged ones, so underperformance is caught at signal detection rather than at weekly review.
Einstein Engagement Scoring analyses recipient-level engagement patterns for each campaign segment: who is opening, who is clicking, who has gone cold, and who is showing unsubscribe intent before they actually unsubscribe. The agent uses these scores to identify segments that are responding well (candidates for frequency increase) and segments showing fatigue signals (candidates for suppression or re-engagement flow diversion). Journey fatigue prediction — identifying when a contact is approaching send-frequency tolerance — prevents deliverability degradation before it starts.
For campaigns flagged as underperforming, the Atlas Reasoning Engine runs a diagnostic analysis: is the underperformance in open rate (subject line or send time issue), click rate (content or CTA issue), conversion rate (landing page or offer issue), or unsubscribe rate (audience fatigue or messaging mismatch)? Atlas cross-references the signal pattern against your historical campaign data to identify the most likely root cause. This diagnostic determines what action the agent takes — targeting adjustment, send time shift, content flag, or human escalation for structural issues.
Within configured parameters, the agent executes optimisation actions without human approval: suppresses cold segments from the active send list (contacts with zero engagement in the last 30 days), shifts send times to the Einstein-predicted optimal window for each segment, adjusts audience filters to exclude contacts showing unsubscribe intent, and diverts fatigue-signals contacts into a re-engagement journey rather than continuing the primary sequence. All autonomous actions are logged with the reasoning that triggered them. A daily optimisation summary is posted to the configured Slack channel so your team sees what the agent did overnight.
For campaigns showing critical signals — unsubscribe rate above threshold, deliverability risk indicators, or a sudden drop suggesting a broken content element — the agent pauses the journey and fires an immediate Slack alert to the marketing team. The alert includes: the campaign name, the specific signal that triggered the pause, the agent's diagnostic (likely root cause), and a recommended action. The agent does not attempt to autonomously fix structural campaign issues — it pauses to prevent further damage and hands off to your team with full context. The journey remains paused until a human resumes it.
The agent generates daily and weekly campaign performance summaries — not raw data exports, but interpreted summaries: which campaigns are performing above benchmark, which have been optimised (and what changed), which are paused and why, and the top insight for the week (e.g. 'Tuesday 10am sends are outperforming all other times by 34% for your enterprise segment'). Summaries are distributed to the configured recipients via email and Slack. Monthly summaries include trend analysis across the full campaign portfolio — performance trajectory, segment health, and optimisation impact.
Performance diagnosis
Diagnoses why a campaign is underperforming — isolating whether the issue is in subject line, content, audience, send time, or offer — and determines the correct optimisation action.
Segment scoring
Scores every recipient's engagement level and predicts fatigue — so the agent can suppress cold contacts and protect deliverability before unsubscribes spike.
Campaign platform
The native platform where journeys run. The agent reads engagement signals, adjusts audience filters, controls journey pause/resume, and triggers re-engagement flows.
Real-time signal feed
Feeds real-time engagement events into the agent's monitoring pipeline — open events, click events, conversion events, and unsubscribe signals.
Alerts + summaries
Delivers journey pause alerts and daily/weekly performance summaries to your marketing team — with diagnosis context, not just metrics.
Optimisation execution
Executes suppression list updates, send time adjustments, and re-engagement journey triggers as automated Flow actions within Marketing Cloud.
Report generation
Generates interpreted performance summaries — not raw data but narrative insights grounded in campaign metrics and benchmark comparisons.
Kovil AI scopes, builds, tests and deploys this Agentforce configuration end-to-end. You do not touch Agent Builder until it is live and optimising campaigns.
Autonomous actions (within your configured parameters): segment suppression of cold contacts, send time shifts within a configured window, diversion of fatigue signals to re-engagement journeys, and suppression of unsubscribe-intent contacts from primary sends. Human approval required: pausing an active journey (the agent pauses and alerts, not resumes), changing campaign content, modifying the core audience definition, and any budget reallocation. The boundary between autonomous and human-approval actions is configured during implementation and can be adjusted.
Three mechanisms: journey fatigue prediction (suppressing contacts approaching their tolerance threshold before they unsubscribe), unsubscribe intent detection (suppressing contacts showing pre-unsubscribe engagement patterns), and immediate journey pause on deliverability risk signals (sudden spike in spam complaints, bounce rate increase). These are the three most common causes of deliverability degradation — all three are monitored continuously.
Yes. The agent can be configured with separate benchmark thresholds and optimisation rules per brand, campaign type (transactional vs promotional vs nurture), and audience tier. Enterprise clients typically configure separate rule sets for different product lines or regional audiences.
A Slack message with: total campaigns active, campaigns performing above benchmark (with the top insight), campaigns optimised overnight (with what changed), and any campaigns paused (with reason). It takes 60 seconds to read and gives your team full situational awareness before their morning standup. The weekly summary adds trend lines — whether overall campaign health is improving or declining across the portfolio.
Book a 30-minute discovery call. We'll audit your current campaign performance, identify the optimisation opportunities your weekly review is missing, and scope a 3-week fixed-price Agentforce implementation.
3-week sprint · Fixed-price · Production-grade · Post-launch support included