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Your AI agents work 24/7. Your access to them doesn't. You don't need more agents. You need to reach the ones you have when the window is open. Most performance teams frame this as a trade-off. The agent sessions are there when you're at your desk. That framing hides the cost. Here's what's running while you're not at your desk: • Automated rules firing and campaign health scores shifting • Google's Smart Bidding adjusting bids in real time on signals you can't see • A PMax campaign that read stable at 5pm telling a different story by midnight if a feed issue crept in The accounts don't need most overnight attention. They need some of it. Specifically, the decisions with a sub-hour window. A bid strategy switch pushed to morning means you've missed tomorrow's peak. A budget pacing alert at 10pm seen at 8am bled all night. A product feed failure excluding your top-5 SKUs from Shopping costs proportionally to how long it runs. We manage $10M+ across 20+ brands with 3 operators. Mobile access to agent sessions - we use Claude Code via Telegram - collapsed decision latency from 8 hours to 10 minutes. In practice: a pacing alert fires at 11pm. The operator on rotation pulls the account snapshot from their phone, sees a feed issue excluding 40 SKUs from PMax, and queues the refresh. It runs overnight. Account is clean by morning. The question for every performance team: which decisions in your ops need sub-hour latency, and does your setup support that? Try our agent fleet at tegra.co/agents. $3-4/run.

The cleanest unit for an AI agency offer is one trigger-to-outcome workflow. Meta's Business Agent just crossed 1 million businesses, and the operating layer behind that chatbot is where the value sits. Meta is connecting agents to systems like Shopify and Zendesk so they can answer, recommend, qualify, book, and act. Take one real agency job: a spend-pacing exception. The trigger is a campaign crossing its approved pacing band. The agent reads the ad platform, inventory state, and promotion calendar. It diagnoses the exception, stages a bounded response, routes it to the named account owner, then records whether the approved action restored pacing. That is one billable unit because its scope can be inspected: • One trigger • Named read access • Named write access • One approval owner • One terminal outcome Most productized services stop at a template. The better product carries the client from signal to approved action and records the outcome. That is more defensible than selling access to the same model every competitor can buy. Every system we run at Tegra ends in this shape. The agent stages; a named human approves anything that touches money or the public. The boundary is the deliverable. The chatbot is the demo. It also changes onboarding. The first call maps truth sources, decision rights, and terminal conversions before anyone asks the agent to produce anything. Price the build around the mapped workflow. Price maintenance around the systems, rules, and platform changes that can break it. We haven't seen many agency scopes written this way yet. That gap is the offer.

Most performance agencies are at Level 2. They think they're at Level 3. Level 3 in Eric's 4-level model is "company brain" - shared context, agent roles, SOPs documented. Most shops have Notion. Most have agents running. The company brain in performance marketing is the attribution data from last month's winning campaigns, not your SOP doc. The performance-specific four levels: • Level 1: AI writes copy - faster execution, same intelligence • Level 2: AI runs roles like creative, bidding and reporting - parallel execution, each run starts at zero • Level 3: agents share context like SOPs, brand rules and past winners - better output, attribution still offline • Level 4: attribution feeds the brief, the ad, the bid and the ICP - closed loop, the system learns from every campaign The jump to Level 4 was one architectural change: wiring attribution data into the feedback loop. In our setup, search term signals update creative angles, creative output feeds bidding, bidding results update the ICP, and the ICP improves the next brief. Each layer compounds the last. When we ran AI-assisted execution without that wire-up, performance plateaued. The attribution test: does last month's conversion data automatically improve this month's first brief? Three of us manage $10M+ across 20+ brands. That wire-up moved us from faster execution to compounding performance.