Agentic
Working together with AI agents — from project delivery to AI-native processes
Agentic does two things most tools keep separate. It gives you an organized team of AI agents that plans and delivers project work — research, documents, presentations, analysis. And it gives you a studio where the processes you design are built and run as living, AI-powered workflows: scheduled, triggered, connected to your systems, with people approving the steps that matter. Design and implementation, in one place.
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Your AI consulting team
Instead of a single chatbot, Agentic organizes its agents the way a consulting firm organizes people, so larger pieces of work can be broken down, delegated, and assembled: a Principal coordinates the project and talks with you, Managers own workstreams, and Consultants do the focused task-level work. A project moves through a simple, controlled rhythm:
Set up
Describe the goal in plain language and add your materials — upload files, connect Google Drive, record a voice note, or talk it through in a spoken interview.
Plan
The team proposes a project plan. You review, edit, and approve it before substantial work begins.
Execute
The team runs the plan in focused work sessions — individually, in batches, or hands-off on autopilot across many deliverables at once.
Review & refine
Results come back as drafts. You comment, iterate, and polish — including an AI editor that can rework a document or a precisely marked area of a single slide.
Along the way the team researches companies, topics, and people; flags missing information and drafts requests for it; and keeps everything grounded in a shared document library. You can type or talk — dictation, voice notes, live interviews, even a listening assistant that follows a meeting.
A project partner, not a task assistant
Many AI tools wait for an instruction, do one step, and wait for the next. Agentic is built for whole projects. The agents coordinate work across workstreams against the approved plan, and a live monitoring view reads the state of the project and suggests what could usefully happen next — a deliverable worth iterating, missing information worth requesting, a piece of research worth running. Each suggestion is one click away from action, and just as easy to ignore. The system works ahead of you instead of waiting to be told.
The AI Studio — where processes become software
Delivering a project is often only the beginning. The deeper change comes when a recurring process — intake, review, reporting, onboarding — stops being manual and starts running as an AI-native workflow. You describe the process in conversation; Agentic helps design it, builds the missing pieces, and turns it into something that runs.
Workflows
Multi-step processes combining AI steps, scripts, human approvals, and actions in connected systems — designed in conversation, then versioned, activated, and run.
AI Jobs
A single, repeatable AI task with defined inputs and outputs — run it again and again, on its own or as a workflow step.
Agents
Reusable specialist designs — researcher, presentation builder, document builder, manager — that can staff project teams or power workflow steps.
Skills
Packaged know-how and helper scripts agents and jobs use — customize built-ins or create your own from a plain-language description.
Solutions
Small custom tools and apps — dashboards, calculators, data scripts — built from a natural-language brief and callable from workflows.
Reports
Forms, run reports, and live cockpit views to launch, track, and review every run.
Workflows start in whatever way fits the process: on a schedule, from a webhook or system event, through a form, via an API call, or by hand. They pause for human review where judgment is required — approvals land in a single Operations inbox — and they act on connected systems such as Gmail and Google Drive to move documents and send messages.
Example — vendor onboarding, AI-native
A supplier submits an intake form. The workflow validates the submission, an AI Job scores the vendor's risk, a script turns the score into a clear rating, and a human approver reviews the case in the Operations inbox. On approval, the workflow files the documents and emails the requester — automatically, every time, with a full record of every run.
Each piece — the scoring job, the rating script, the approval step — is a reusable building block. The next process you automate starts faster because the library of jobs, skills, agents, and solutions grows with every project.
The best of the AI ecosystem — plus engines of our own
Agentic is not tied to a single AI provider. It orchestrates the strongest model for each kind of work.
Language models
The latest models from the leading labs — OpenAI, Anthropic, Google, xAI — alongside strong open-source alternatives, selected per task. You can override the choice for any run.
Image & voice
Leading image-generation models for visuals, and real-time voice models that power dictation, live interviews, and spoken discussions.
Presentations
Quality providers such as Gamma and SlideSpeak, plus Agentic's own engines that produce image-quality slides that remain fully editable — a combination you will rarely find elsewhere.
For consultancies and transformation teams
If you advise clients on their AI journey, Agentic changes what you can hand over. The same platform that helps you run the engagement — discovery, analysis, deliverables — also lets you implement the target processes you design, as working workflows the client can see, test, and operate. Instead of a report that describes an AI-native process, the client receives the process itself: running, measurable, and refinable.
You stay in control — and you can trust the output
You approve the plan
Nothing significant runs until you approve the plan — and workflows pause for human review wherever you put a checkpoint.
Drafts, not surprises
Results come back as drafts you review and refine; every workflow run leaves a visible trail.
Grounded in your data
Output is grounded in your own documents and data, so it reflects your facts rather than guesses.
Independent citation review
Claims are extracted and checked against the cited sources; flagged issues are visible in the library, and anything questionable can be handed straight back to the team to fix.
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