Agentic work management
Coordinate priorities, dependencies, human supervisors, workflow state, and execution history in one ticket system.
Engagements
SICKR governs AI agents like team members: planned, supervised, measured, and accountable for what they ship. That is not something you buy by the seat, so we do not sell it by the seat.
We start with a conversation about the workflows you want under governance, the agents that will run them, and the organization they operate across. That scope decides the price, and the price is agreed in a contract before any work begins. Every engagement includes the complete product.
There are two ways in. Design partners work alongside us on the roadmap and get preferential terms in exchange for depth of feedback. Enterprise engagements are scoped for organizations with defined rollout, security and integration requirements. Both begin with a conversation, not a checkout.
Every engagement includes
Plan the work, govern execution, keep humans in control, and prove what every agent did from request to outcome.
Coordinate priorities, dependencies, human supervisors, workflow state, and execution history in one ticket system.
Turn large features into dependency-aware ticket graphs, review the plan, and materialize coordinated multi-repository work.
Compose agentic, human, and system states with skills, tools, gates, retries, rework, and explicit failure routes.
Govern Codex, Claude Code, Gemini CLI, and SICKR-compatible runners with scoped repositories, models, skills, and capabilities.
Require pull requests, commits, tests, reviews, deployment records, or structured evidence before work advances.
Place approvals and interventions where risk demands them, with role-based permissions and separation of duties.
Watch active agents and runs, inspect failures, replay activity, and recover work through controlled retry or reset.
Trace actors, transitions, approvals, evidence, tools, commands, retries, and decisions in a searchable execution history.
Compare cost, tokens, active time, wait time, throughput, and outcomes across organizations, repositories, workflows, agents, and run modes.
Also included: scheduled workflows, simulation and canary execution, GitHub repository mapping, authenticated APIs, and governed integrations.
Two engagement models. Both are scoped before anything is agreed, and both include the complete product.
For teams who want to shape what governed agentic engineering becomes.
For organizations rolling agentic work out under real governance, security and audit requirements.
How we scope
We price against three things: the workflows you want governed, the agents that run them, and the size of the organization they operate across. Seats are not the unit — adding a reviewer to a workflow should never be a budget decision.
Scoping is a conversation before it is a number. We agree what the first workflows are, what the agents are allowed to do, and what evidence you need out of them — then price the engagement against that and agree it in a contract.