Goals before workflows
Turn goals into reliable outcomes
Cortexium is being built to combine AI reasoning with deterministic functions, your tools and human review. The goal is useful work with results you can inspect.
Built around outcomes
- Start with a goal, request, event or task and define what a good result looks like.
- Use AI reasoning for research, interpretation and judgment.
- Give validation, transformations and repeatable procedures to deterministic functions.
- Coordinate agents, tools and people around the work.
- Keep the result and the evidence needed to review it.
Keep control of the work
- Set clear boundaries for each agent and tool.
- Use human approvals for risky or external actions.
- Make failures and missing information visible.
- Review traces, artifacts and evaluations before accepting a result.
- Preserve useful decisions and approved processes as scoped memory.
Problems we focus on
Where agents actually help
Multi‑step tasks across tools
Work that spans several steps and services.
Glue work between apps
Move, transform, and hand off data.
AI + rules in one process
Language tasks with checks and logic.
Human approvals in the loop
Steps that require review, sign‑off or human-in-the-loop involvement.
Automate repetitive tasks
Run the same operation many times, with oversight.
Data in / data out across systems
Pull, enrich, validate, and push back.
Patterns
Match the work to the right approach
Use AI where judgment helps. Give repeatable work to deterministic functions, with clear handoffs and checks between them.
- A focused task with clear inputs, outputs and allowed tools
- AI judgment for research, classification or a draft that needs review
- APIs, MCP tools and custom functions for access to other systems
- Validation and evidence to help assess the result
- Named responsibilities for agents and people
- Explicit handoffs with the context the next task needs
- Scoped knowledge and decisions rather than an ever-growing conversation
- Review of the combined result against the original goal
- Functions for validation, transformation, API calls and business rules
- AI reasoning only at steps that need it
- Defined failure paths and approval points
- Structured outputs that another system or person can use
The big building blocks
Specialized agents
Focus AI reasoning on a clear responsibility. Agents can use tools and call other agents, with explicit inputs, outputs and boundaries.
Projects & teams
Bring agents, people, tools and processes together around a goal. Define who owns the work, who reviews it and what a good result means.
Deterministic functions
Use ordinary code for validation, transformations, API calls and repeated procedures. Prefer deterministic execution wherever judgment is unnecessary.
Processes
Define repeatable steps, checks, failure paths and human approvals. Combine functions and AI reasoning around the outcome the process needs to produce.
Tools & integrations
Use APIs, MCP tools and custom functions with typed inputs and outputs. Give each tool an explicit purpose, scope and permission boundary.
Memory & evaluation
Preserve useful decisions and approved processes as scoped memory. Use traces, artifacts and evaluations to inspect results and check changes.
An example
Review a support request
This illustrative process shows the approach: AI helps interpret the request, while code handles checks and a person approves an external action.
Inspect & improve
Make the work reviewable
A result needs something you can check
The design starts with inspectable results: useful artifacts, relevant inputs, tool observations and the decisions behind the work.
People need to correct, reject or approve a result without losing work they have already accepted. Missing evidence and failed checks should be visible.
Evaluations compare the outcome with the task it was meant to solve. They also help check whether a change to a model, function or process improves the result.
Human governance sets the constraints and approval points for consequential actions. Routine execution belongs within those agreed boundaries.
FAQ
Integration principles
Tools with explicit boundaries
Connecting a tool should make its purpose, permissions and effects clear. These principles guide how Cortexium brings other systems into the work.
APIs
- Typed inputs and outputs
- Clear usage guidance and permission scopes
- Deterministic code for API calls and validation
Use MCP tools
- Connect Model Context Protocol–compatible tools
- Standardize capabilities exposed to agents
- Keep tool permissions explicit
Authentication
- Use the permissions a task actually needs
- Keep credentials out of model context
- Resolve authentication in the integration layer
Custom functions
- Prefer deterministic code for repeatable work
- Typed inputs/outputs with examples
- Reuse across agents, teams, and flows
Requests & results
- Give a request or event a defined outcome
- Validate inputs before execution
- Return structured results another system can use
Checks, logs, approvals
- Validations and routing with clear failure paths
- Logging for decisions and tool calls
- Human approvals for risky or external actions
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