When COSMO makes sense
COSMO is designed for structured, multi-step work—not quick questions. Here's where it fits and where it doesn't.
Good use cases
Structured research projects
When you have a research topic with clear phases: gather information, analyze findings, produce deliverables. COSMO executes this as a plan with tracked progress.
Document generation from research
When you need to produce documents based on gathered information. Reports, analyses, summaries—where the output requires collecting and synthesizing information first.
Knowledge base building
When you want to build persistent, queryable knowledge on a topic. COSMO creates a "brain" you can come back to—ask questions, explore connections, continue building.
Multi-step analysis
When analysis requires sequential steps—where later analysis depends on earlier findings. COSMO's tier system ensures dependencies are honored.
Code + research hybrid tasks
When work involves both research and code generation. The IDE Agent handles file creation while the Research Agent gathers external information.
Not the right fit
COSMO isn't designed for every AI use case. Here's when to use something else:
- Quick questions — If you just need an answer, use ChatGPT or Claude. COSMO is overkill for "what does this function do?" or "how do I format a date in Python?"
- Back-and-forth conversation — COSMO executes plans, not conversations. If you want to iterate interactively, a chat interface is better.
- Immediate results — Task execution takes time. If you need something in the next 5 minutes, COSMO isn't the tool.
- Simple, single-step work — If your task is "write a function that does X," just ask an AI assistant directly. COSMO's structure adds overhead you don't need.
- Unstructured exploration — If you don't know what you're looking for and want to wander, COSMO's plan-driven approach may feel constraining.
What makes COSMO different
Plan-driven execution
You define the structure. COSMO executes it with proper dependency management. This is more predictable than open-ended AI chat.
Persistent knowledge
Results don't disappear. The brain persists as a queryable artifact. You can come back, ask questions, continue building.
Execution transparency
Watch tasks execute in real-time. See what's being researched, what's being created. No black box.
Reliability focus
State management, retry logic, crash recovery. The system is built for tasks that take time and shouldn't fail silently.
Typical workflow
Here's what using COSMO actually looks like:
- Define your task — Write out what you want, including phases if you have them. Be specific about deliverables.
- Configure depth — Decide how many cycles to run. More cycles means deeper investigation but longer runtime.
- Launch and watch — Start the run. Monitor progress as tasks execute. See research happening, files being created.
- Review outputs — When execution completes, review what was produced. Documents, code, analysis—whatever you requested.
- Query the brain — Ask questions about what COSMO learned. Explore the knowledge graph. Dig into specific findings.
- Continue or export — Run additional tasks against the same brain, or export your results for use elsewhere.
Think COSMO might fit?
If you have structured work that would benefit from plan-driven execution and persistent knowledge, let's talk.
Get in touch