Tool silos
Single AI tools do not create reliable end-to-end workflows.
AI Agent Orchestration
Turn repetitive work into controlled AI agent workflows
We design agent workflows that connect source systems, APIs, QA checks, approval gates, logs and human override so teams can use AI with control.
Selected experience includestrivagoCoIQinneOviva
InputBrief, data, files
Research AgentCollects and analyzes information
Analysis AgentStructures and evaluates
Drafting AgentCreates drafts and outputs
QA AgentChecks quality
Human ApprovalReview and release
ExecutionPublishing and automation
Why AI pilots stall
Context breaks
Inputs, decisions and files are passed inconsistently between steps.
No QA layer
Outputs are used before quality, policy or brand checks happen.
No approval gate
Teams do not know where human judgment belongs.
No logs
When something fails, nobody can inspect what happened.
Control model
Source systems
Define which CRMs, documents, dashboards, inboxes or databases the workflow can read.
APIs and tools
Specify the actions agents may trigger and which systems require human confirmation.
Agent roles
Split research, drafting, checking and execution into clear responsibilities.
QA checks
Add quality, consistency, factual and policy checks before release.
Approval gate
Route important outputs to a named human owner before publishing or sending.
Logs and override
Record inputs, outputs, decisions and exceptions so a person can pause, correct or reroute the workflow.
Typical workflows
CRM enrichment agent
Researches accounts, drafts updates and routes changes for review.
Slack approval workflow
Sends drafts, exceptions and decisions to the right owner before execution.
Knowledge agent for Notion or Drive
Finds, structures and cites company knowledge for repeated tasks.
Reporting QA agent
Checks dashboards, flags anomalies and prepares decision-ready summaries.
n8n or Make automation pilot
Turns a repeated process into a tested workflow with human gates.
Content repurposing workflow
Converts approved assets into channel-specific drafts with QA and approval.
Deliverables
Engagement models
Opportunity map
Prioritized workflows based on frequency, risk, data access and expected leverage.
Workflow blueprint
Roles, handoffs, states, approval points and exception paths.
Integration spec
Required tools, APIs, permissions and data sources.
Prompt and guardrail library
Reusable instructions, examples, checks and escalation rules.
Pilot workflow
A working first version tested on real tasks with human review.
Operating playbook
Documentation for owners, cadence, QA and continuous improvement.
Discovery Sprint
Map processes, evaluate opportunities and define prioritized workflows.
Map an Agent Workflow →Prototype Build
Design, test and validate one controlled workflow.
Map an Agent Workflow →Agent Operating System
Scale governance, enable teams and improve the system over real usage.
Map an Agent Workflow →Ready to map an AI agent workflow?
Identify the repetitive process, define the control model and build a first workflow with QA and human approval.