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.

Example task definition: "Research competitive landscape for [product category]. Phase 1: Identify top 10 competitors and their positioning. Phase 2: Analyze pricing models and feature sets. Phase 3: Produce comparison matrix and strategic recommendations."

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.

Example task definition: "Create a technical overview of [technology]. Research current implementations, gather performance benchmarks from published sources, then produce a document covering architecture, use cases, and limitations."

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.

Example task definition: "Build a knowledge base on [domain]. Start with foundational concepts, then research current developments, key players, and emerging trends. I want to query this ongoing."

Multi-step analysis

When analysis requires sequential steps—where later analysis depends on earlier findings. COSMO's tier system ensures dependencies are honored.

Example task definition: "Analyze [dataset/topic]. First, identify key patterns and anomalies. Then, research potential causes for the patterns found. Finally, produce an analysis report with recommendations."

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.

Example task definition: "Research best practices for [technical problem]. Then create implementation code following those practices, with documentation explaining the approach."

Not the right fit

COSMO isn't designed for every AI use case. Here's when to use something else:

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:

  1. Define your task — Write out what you want, including phases if you have them. Be specific about deliverables.
  2. Configure depth — Decide how many cycles to run. More cycles means deeper investigation but longer runtime.
  3. Launch and watch — Start the run. Monitor progress as tasks execute. See research happening, files being created.
  4. Review outputs — When execution completes, review what was produced. Documents, code, analysis—whatever you requested.
  5. Query the brain — Ask questions about what COSMO learned. Explore the knowledge graph. Dig into specific findings.
  6. 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