Agents scoped narrow enough to trust
Marketing, sales, research, and support agents scoped narrowly enough to actually trust in production.
An AI agent is software scoped to one specific, repeatable task - marketing, sales, research, support, or a custom workflow - with defined guardrails and a human escalation path, built narrow enough to actually trust in production rather than a broad assistant that needs constant supervision.
The useful version of an AI agent handles one narrow, repeatable task reliably - not a general-purpose assistant that needs constant correction. Scope is the entire game here; a broad mandate produces an agent nobody trusts enough to rely on.
We build agents around specific functions - marketing, sales, research, and support - and reach for a custom build only when none of those patterns fit the actual workflow. Every agent ships with guardrails and a defined escalation path, because the failure mode we design against is an agent confidently doing the wrong thing, not one that says 'I'm not sure, let me get a person.'
Problems we solve in AI Agents
Over-scoped agent mandate
A system asked to plan, write, and decide everything together instead of one narrow task.
Confidently wrong output
An agent given too much latitude eventually acts or answers wrong with nobody catching it in time.
No review checkpoint
Output ships or actions execute without a human check until the error rate is proven low.
Undocumented boundaries
No short list of exactly what the agent is and is not allowed to do.
Handoffs that lose context
A person has to start over because the escalation carries no history with it.
A workflow forced into a template
A process specific to the business gets bent to fit a generic agent pattern instead of being scoped around directly.
Everything covered in AI Agents
AI Marketing Agents
Content drafting and campaign monitoring agents scoped to one task, with a human checkpoint before anything ships.
AI Sales Agents
Lead qualification, follow-up, and scheduling agents that hand off to a rep at exactly the right moment.
AI Research Agents
Competitive, market, and account research agents that gather, verify, and structure findings at a scale manual digging cannot match.
AI Customer Support Agents
Ticket-resolution agents grounded in your knowledge base, escalating anything they cannot verify.
Custom AI Agents
Agents built from the ground up around a workflow no off-the-shelf platform supports.
What actually changes
| Before | After |
|---|---|
| A system attempts planning, writing, and deciding together | One defined task with a short list of allowed actions |
| Errors are hard to predict and easy to miss at volume | Errors are rare and caught by a review queue sized to the task |
| The agent produces a confident answer regardless of certainty | The agent escalates automatically the moment it is outside scope |
| A handoff carries no context, so a person starts over | A handoff carries full context to whoever picks it up |
| A workflow gets bent to fit a generic platform | The agent is scoped and built around the actual workflow |
What we report on
Review queue error rate
Tracked against the approval queue to confirm the agent stays inside its documented scope.
Escalation accuracy
How reliably the agent flags work outside its scope instead of guessing.
Time offloaded from manual work
Measured against what the task previously took on a person's plate.
Workflow coverage
The share of a mapped process the agent handles without manual override.
Tools we work with
Every AI Agents service, broken down
Agents That Actually Do the Work, Not Just Suggest It
AI marketing agents scoped to one repeatable task at a time, with a human checkpoint before anything ships.
AI Sales AgentsNever Let a Lead Wait Hours For a First Response
AI sales agents that qualify, follow up, and schedule, then hand off to a human rep at exactly the right moment.
Hours of research, compressed to minutes
AI research agents for competitive analysis, market research, and account research, run at a depth and scale manual digging can't match.
Close the Repeat Tickets. Escalate What Needs a Human.
AI support agents that close out tickets that repeat constantly and hand off anything genuinely novel cleanly.
Custom AI AgentsGet an Agent Built Around Your Workflow, Not a Template
Custom AI agents for internal or customer-facing workflows off-the-shelf platforms can't handle.
Industries we work with
Built for teams like yoursPairs well with AI Agents
Leads nurtured while you sleep
CRM workflows, lead scoring, and campaign automation that move MQLs to SQLs without manual follow-up.
Your stack, working as one system
API, CRM, marketing platform, and payment integrations so nothing requires manual copy-pasting between tools.
Clean data, before anything else
GA4, GTM, and BigQuery implementations that make every downstream decision trustworthy.
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
A basic automation follows a fixed if-this-then-that path with no judgment involved. An agent uses a language model to decide what to do within a scoped set of options - which lets it handle variation a rigid workflow can't, without the open-ended unpredictability of a general chat assistant.
Narrow enough that you could write down every input it will see and every action it's allowed to take. If that list is short, the agent will be reliable. If you can't write it down without hedging, the scope is still too broad and needs to be split into smaller pieces.
It depends on the task. A research or support agent pulling from existing documents needs those documents organised and accessible, but not a full data platform. A sales or marketing agent that needs to write to a CRM needs that integration working first - we check this during scoping rather than assuming it's ready.
It escalates to a person instead of guessing. Every agent we build ships with a defined escalation path, and the situations that trigger it are decided upfront, not discovered after something goes wrong in production.
No - any system built on a language model will occasionally get something wrong, and a vendor promising otherwise is overstating it. What we can guarantee is that the agent is scoped narrowly enough to keep errors rare and contained, and that it escalates rather than acts confidently when it's uncertain.
A single-task agent with a clear scope - one integration, one review workflow - usually takes four to eight weeks including a monitored pilot period. Multi-step agents or ones requiring several system integrations take longer, and we scope that specifically rather than quoting a blanket timeline.
Considering an AI agent for a specific task?
We scope it narrow first - a reliable agent that does one thing beats a broad one that needs constant supervision.
Ready to get started?
We usually reply within 24 hours.
AI Agents, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Task Scope | A written definition of the one task the agent handles and what triggers escalation |
| Agent Build | A working agent integrated with the actual tools and systems the task needs |
| Review Workflow | A human approval queue sized to the task's risk and your team's capacity |
| Monitoring | Ongoing accuracy and drift tracking, not a one-time handover |
How this compares
| Scoped Agent | Broad AI Assistant |
|---|---|
| Handles one defined task with a short list of allowed actions | Attempts planning, writing, and deciding together |
| Errors are rare and caught by a sized review queue | Errors are harder to predict and easier to miss at volume |
| Escalates automatically when outside its scope | Tends to produce a confident answer regardless of certainty |
A broader agent is not more capable, it is just harder to trust, because there is no short list of things to check its work against.
What we measure this against
- Review queue error rate and escalation accuracy against the documented scope
- Time offloaded from manual work on the specific task
- Workflow coverage handled without a manual override
Where this applies
- A marketing or sales team has a repeatable task eating time that a scoped agent could handle with a human checkpoint
- A support queue is full of the same handful of verified, repeatable questions
- A team needs research on dozens of accounts at a depth manual digging cannot reach
- A workflow is specific enough that no off-the-shelf agent platform actually fits it