How to Configure Okki Go in an AI Agent: A 7-Step Contact Discovery Checklist

Who This Checklist Is For

If you're setting up Okki Go inside an AI agent for B2B prospecting, and you want to actually get it working — this is the checklist. Seven steps. No fluff upfront.

I manage quality review for the prospecting pipeline at a mid-market outbound shop — we run roughly 40 client campaigns a year, and I'm the last person who touches a lead list before it hits the sequencer. In 2024, I rejected about 27% of first-pass automated output. Most of those rejections traced back to configuration choices someone made in an agent setup and never revisited.

That's the whole point of this list: the config decisions you make on day one are the ones that keep biting you in month three.

The 7 Steps

Step 1: Define Your ICP in Structured Fields, Not Paragraphs

Before you open Okki Go, write your ICP as structured filters. Not "mid-market SaaS companies in North America." That means firmographic fields — headcount range, HQ country, tech stack, funding stage — plus a title map (who you want: VP Sales, Head of RevOps, Director of Demand Gen) and an exclusion list.

The exclusion list is the one people skip. Competitors, current customers, churned accounts inside a cooling-off window, agencies that resell — all need to be explicit filters. An agent will happily source 200 leads from a company you fired last quarter.

Checkpoint: every field in your ICP should be something Okki Go can filter on. If it can't, it's a note, not a criterion.

Step 2: Wire Up Your Contact Data Layer

Okki Go's contact discovery is only as good as the data sources behind it. Two decisions here:

1. Single source or waterfall? Single-source is simpler and cheaper per record. Waterfall enrichment — chaining multiple providers and taking the first valid hit — typically raises contact coverage by 15–30% in our campaigns, at a higher per-record cost. For a tight ICP, waterfall wins. For a broad ICP, single-source is often fine.
2. Verification: inline or post-process? Do not skip this. Configure your email lookup tool to verify every address before it enters the sequence. The difference between a 2% and an 8% bounce rate almost always comes down to whether verification runs at ingest or gets skipped.

As of Q1 2025, most reputable verification providers report bounce-rate accuracy in the low single digits when used correctly. Anything claiming "100% deliverability" is a red flag, not a feature.

Step 3: Configure Contact Discovery Rules

This is where Okki Go does its actual work. Three sub-configs:

• Pattern matching: set your domain matching logic (e.g., strip www, collapse subdomains, handle co.uk correctly). Miss this and you'll source contacts from subsidiaries that have nothing to do with the parent you targeted.
• Title normalization: map variations of the same role — "VP of Sales," "VP Sales," "Sales VP," "SVP Sales" — to a single target role. Without this, your coverage looks spotty when it's really just a naming problem.
• Fallback chain: define what happens when the exact contact isn't found. Fall back to a same-domain alternative with a matching role? Skip? Flag for manual review? Most teams never set this, and the agent defaults to something you didn't choose.

Step 4: Set Intent Signal Filters

Intent data is noise unless you filter it. Configure thresholds before you let the agent act on them.

"Everything I'd read said more signals equals better targeting. In practice, with our 40-client book, the campaigns with the tightest intent filters — two signals max, both required — consistently outperformed the ones that triggered on any of eight."

Decide what counts as a hot signal for your ICP (hiring for a specific role, tech stack change, funding event, job post frequency) and how many signals must overlap before a contact enters the sequence. One signal is a coincidence. Two overlapping signals inside 30 days is a real pattern.

Step 5: Connect Your Email Lookup Tool the Right Way

Okki Go works best when it's feeding a verified address into your engagement layer, not guessing. Configure the handoff so the email lookup tool runs before the contact enters the sequence, and log the verification timestamp on each record.

I only believed this after ignoring it once. In March 2024, we pushed a batch through without the verification step because we were behind on an event deadline. Two hundred and twelve contacts, one campaign. The bounce rate came back at 9.4% — well above the 2% range our sending domain was configured to tolerate. We spent the next six weeks repairing sender reputation. That's a lesson I don't need twice.

Step 6: Build the Sales Engagement Sequence Around Agent Output

Most sales engagement platform features worth configuring live at this layer — step timing, channel mixing, reply detection, auto-pause on positive intent. The mistake is treating the agent as the strategist. It's not.

Okki Go sources and enriches. Your sequence decides what happens next. Configure:

• Step cadence (typical: 4–6 touches over 12–18 business days)
• Channel mix (email-only vs. email + LinkedIn touch)
• Reply handling rules (auto-pause on any reply, not just positive ones)
• Suppression rules (replied, closed-lost window, do-not-contact list)

The question isn't whether your sequence is clever. It's whether it stops when a human answers.

Step 7: Add a Human-in-the-Loop Review Gate (Most Teams Skip This)

This is the one I'd fight for in any setup. Not full manual review — that defeats the point of an agent. A sampled gate.

Configure your agent to route 5–10% of output to a human reviewer before it hits the sequencer, weighted toward edge cases: previously-flagged domains, contacts from outside your primary ICP, and any batch above a certain size. The reviewer checks three things — domain validity, role fit, and whether the intent signals are still live. Anything failing two of three gets rejected and fed back as a training signal.

That gate is what kept our client renewal rate above 90% through 2024. It's also the difference between an agent that learns and one that just keeps producing the same mistakes at scale.

Common Mistakes Before You Hit Go

A short list of the things I see break in the first 30 days:

• Skipping verification "just for the first batch." There's no such thing as a first batch. Domain reputation is cumulative.
• Broadening the ICP to hit a volume target. Volume without fit is just a faster way to generate unsubscribes.
• Forgetting to suppress current customers. It happens more than anyone admits.
• Measuring sends, not replies. The only metric the agent should be optimized against is reply rate on verified contacts — not contacts sourced, not emails sent.
• No named owner. If nobody owns the config, nobody updates it — and by month two the agent is running on rules that no longer match your ICP.

If you take one thing from this list: the configuration you set on day one is a hypothesis. Review it. Adjust it. Document what changed and why. The agent doesn't get smarter on its own — it gets smarter because someone upstream decided to check.