A workforce you can inspect.
Meet the role-based agents you can add to your team, the way you would review candidates for a seat you already have on your org chart.
135+ role-based agents, curated by department.
Each agent ships with a description, operating rules, deliverable templates, and quantitative success metrics. Select a department to see the seats we staff first and what each one owns, may decide, and must escalate.
Live in production today.
3 role-based agents. Showing 3 example seats.
Lead scoring, ICP matching, and pipeline qualification.
Lead priority and routing.
Non-standard lead sources and budget exceptions.
Personalized outreach sequences and follow-up cadence.
Channel, timing, and message variant.
Negative signals and opt-out patterns.
Deal progression, proposal assembly, and close support.
Proposal structure and competitive positioning.
Pricing, terms, and commitment authority.
Plus specialist packs:
You are not launching a project. You are adding to the team you already have.
Meet the role-based agents the way a hiring manager thinks: prepare the seat, select the role, stage and activate, then manage the work. A new class of employee, added alongside your people.
Before a good hire starts, you set them up to succeed. Same here.
Context readiness (CLM)
The business context an agent needs to act like it already knows the place, snapped in on day one.
Tools readiness
The systems the seat touches, connected and scoped.
Human readiness
The people who brief, approve, and correct the agent, and the standard they hold it to.
Most AI projects start from zero. We do not.
Abeba maintains a versioned baseline of the construct itself: the agent stack, the Agent Management Center, the governance rails, and the role-based seats that recur across a business. It is built once, held under version control, and eval-gated before it is ever used. When we engage, we are not assembling from scratch; we are staging a known-good environment and specializing it to your business. That is what lets us stand up a functioning AI Forward environment in weeks, not quarters.
Pre-staged does not mean generic. It means versioned and eval-gated. Your agents are specialized to your org chart, your systems, and your standard. What is pre-built is the construct and its guarantees; what is custom is your business.
The construct, pre-built
The five-layer stack, the management center, and the trust rails ship as a versioned baseline, not a per-client rebuild.
The seats, pre-shaped
The role-based agent seats that recur across SMBs are drafted and eval-tested in advance, then briefed onto your org chart.
The speed, earned honestly
Because the baseline is versioned and gated, activation starts on day one and specialization is the work, not scaffolding.
The five-layer stack.
Owns a seat and its outcomes; briefs and supervises the rest.
A narrow, deep operator for one part of the job.
A repeatable procedure the agent runs the same way every time.
The systems the seat touches, connected and scoped.
The Context Language Model: the business context every layer draws on.
Is it an agent?
A real candidate for a seat clears all five. A chatbot clears none of them.
An agent is accountable for an outcome on your org chart, not a one-off reply.
It knows what it may decide on its own and what it must escalate to a person.
It works in the tools you already run, not in a sandbox off to the side.
It draws on the Context Language Model, so it acts like it already knows the business.
It sharpens every week from your team's own feedback, and the gains compound.
Agents act, people approve.
The builder never certifies its own work. Every agent is visible, auditable, and stoppable in the Agent Management Center. Your data stays yours. A new class of employee, added to your team, held to your standard.