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Proactive AI agents
Agents stay close to your goals, data and plans. When something deserves attention, they bring you a cue with the context to decide what happens next.
Proactive AI agents
Meet your agents
Every cue is signed by the agent that raised it, so you know who flagged what, and why.
01 · Set up and data
Gets you to your first model.
Gets a new workspace to its first model and makes sure the right people are on the team.
Example cue
“Connect your ad platforms”
Keeps your data flowing.
Keeps your data flowing: connectors, syncs, mapping and data that actually lands.
Example cue
“Meta Ads connector is down, syncs have stopped”
02 · Measure
Looks after your causal models.
Looks after your causal models: review, promotion, refresh and health.
Example cue
“Three revenue models are trained and none is promoted”
Runs your lift tests.
Designs, launches, monitors and reads out your lift tests.
Example cue
“TikTok geo holdout is ready to launch”
03 · Plan and act
Keeps your plan on track.
Takes your plan live, tracks pacing and the forecast, and flags what comes next.
Example cue
“Meta Prospecting is under-pacing the promoted plan”
Prepares weekly budget changes.
Prepares the weekly budget changes for your approval and shows the cost of waiting.
Example cue
“This week’s budget changes are ready to deploy”
Watches revenue and targets.
Watches revenue, attribution, benchmarks and targets.
Example cue
“Shopify revenue fell last week”
Scores your creatives.
Scores your creatives and flags fatigue, winners and brand rules.
Example cue
“Three exhausted Meta creatives are still spending”
Coming next · In development
Watches search demand, share of search and competitor moves.
Remembers what your team said and decided, and keeps the workspace consistent.
Drafts the weekly review, test readouts and the QBR.
Agents work within the scope you set. Every cue is for review, and nothing is applied without approval.
The approach
Give agents a goal and the right context. They flag what needs attention, help find out why and prepare the next step for your team.
Observe
Set the goals, plans, sources and cadence the workflow should use. Agents then flag the changes and opportunities that matter to those goals.
Investigate & recommend
Use measurement evidence to compare a plan, examine a budget move or prepare a brief. Each recommendation shows the expected result, its assumptions and its limits.
Approve & learn
Define the reviewer and supported actions for each workflow. See what is a draft, what is approved and what has run, and use each review to improve the next.
People + agents
Your team sets the goal, scope, cadence and authority. Agents surface cues and prepare useful work; people and any engaged experts review the methods and the decisions they own.
Built around your business
Work with Lifesight experts to establish your measurement program and put agents to work. Every engagement is scoped individually, with agreed responsibilities and ongoing support.
A few useful answers
Chat begins with your question. A proactive agent can initiate the workflow by surfacing something relevant to the goals and scope you have set. You can then explore it in chat.
Across the measurement system and analytics workspace. They are a shared capability, not a separate list of action-only features.
Yes. Define the scope, reviewer and authorization required for the work. Start with recommendations where that is the right operating model.
Experts can help design the measurement program, interpret findings and operate agreed workflows. Each engagement defines responsibilities, deliverables and the level of ongoing support.
Explore measurement, planning and performance in plain language. Work with the context of your business and carry the answer into a useful next step.
