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One question. Every agent collaborates.
The mechanism behind a Cresva recommendation. How a question routes through the agents, what each agent contributes, and why the answer compounds rather than plateaus.
Single AI plateaus. Agents compounds.
A single model is a generalist. Ask it to forecast revenue, debias attribution, score creative fatigue, and write a recommendation, and it does each task at roughly 70 percent of what a specialist would. Accuracy plateaus there, regardless of how much data you feed it.
Cresva splits the work. Each agent owns one function and shares one memory. Felix gets better at forecasting because Felix only forecasts. Parker gets better at debiasing because Parker only debiases. The shared memory means specialization does not fragment context, and the shared context means the agents answers as one even though the agents reason as seven.
Specialization creates mastery. Coordination creates intelligence.
"Should we shift budget?" What happens next.
A single question routes through the whole team in sequence. Each contributes one slice. The recommendation reads as a decision, not a dashboard.
Should we shift 20 percent of budget from Google to Meta for Q4?
Pulls user context. CAC ceiling at $65, margin floor 15%, Q4 historically +40% revenue. Surfaces past Meta vs Google discussions.
Conversations scanned, relevant context retrieved
Fetches the last 90 days across Meta, Google, Shopify. Reconciles spend and revenue. Meta ROAS 3.2x reported, Google 2.9x reported.
3 platforms queried, 2.4M data points processed
Applies platform debiasing. True incrementality: Meta 2.4x not 3.2x, Google 2.6x not 2.9x. Meta overclaiming by 33%.
Holdout calibration data, last 12 weeks
Top three Meta creatives showing fatigue, CTR down 23% in 14 days. New variant pipeline empty. Risk: CAC spike if Meta share grows.
127 creatives scanned, fatigue flagged by rolling CTR decay
Q4 forecast at current 60/40 allocation: $2.1M revenue, $58 CAC. With 70/30 shift: $2.05M revenue, $71 CAC, breaches the cap.
Elasticity-based scenario simulation
Sweeps the allocation space. The 70/30 shift breaches the CAC cap. Recommends hold and creative refresh first.
Allocation sweep, constraint violations flagged
Compiles the response. Formats for Slack delivery per the user's preference. Logs the recommendation in the brand memory store.
Per-recipient formatting applied, response sent
Hold the current 60/40 allocation. Refresh Meta creatives first. Re-run the analysis in three weeks.
- 70/30 shift puts CAC at $71, above the $65 cap
- Revenue drops $50K versus the current allocation
- Meta's reported "3.2x ROAS" is 2.4x after debiasing
- Top three Meta creatives are fatiguing, CTR down 23 percent
Constraints stay learned. Decisions stay logged.
Maya extracts what matters from every conversation. Constraints, patterns, history. Surfaced when the next agent needs it.
Constraints
- CAC ceiling$65
Stated in conversation #234
- Margin floor15%
Set during onboarding
- Weekend budget−20%
Preference from Q2 review
Patterns
- Q4 revenue multiplier1.42x
Three years of historical data
- Meta creative fatigue21 days avg
127 creatives analyzed
- Google CPC trend+3% / month
14 months of bid data
History
- TikTok test, Aug 2025Failed, $12K loss
Conversation #456
- UGC campaign, Sep 20252.1x ROAS
Performance tracked end to end
- Black Friday 2024$340K revenue
Reconciled against Shopify
Human-approval workflow on every memory write. Nothing learned without a sign-off, and everything written can be inspected, edited, or removed.
Every prediction tracks against reality. Misses become signal.
Every forecast is written down before the period it covers, then scored against what actually happened. A miss is analyzed for cause and the result is fed back into the model, so it learns your patterns rather than getting generically smarter.
Measure.
Every prediction has a timestamp. Every outcome is recorded. The delta between expected and actual is the learning signal.
Identify the cause.
External factor like a competitor sale or platform change? Seasonality? Bad data? The cause determines the fix.
Update the model.
Model weights adjust. Elasticity curves recalibrate. Confidence intervals tighten. The next prediction starts closer.
When agents disagree. Confidence wins.
Multi-agent systems fail loudly when they disagree quietly. Cresva surfaces conflicts instead of hiding them. When Felix and Sam reach different conclusions on the same question, the recommendation includes both calls, the confidence on each, and the reason the agents sided with one of them.
Confidence is not a marketing number. Each agent reports an interval based on backtested accuracy and the data window backing the call. The agent with higher confidence and tighter context wins by default. The dissenting view is shown in the response, not buried.
Q4 forecast: $2.1M revenue at 60/40 allocation.
3-month data window
70/30 shift breaches the CAC cap.
Elasticity-based scenario sweep
The user sees both numbers. The recommendation goes with Sam's call. Felix's forecast is shown alongside, with the score that explains why it was the second-place answer.
Ready when you are
See an agent in action, or run a 7-day pilot.
Each agent has a deep dive. The pilot connects every agent to your real accounts.
Looking for the deep dive? See Felix forecasts → or Parker debiases → or Maya remembers →.