How an Agent Uses Snipara During a Real Task
A concrete walkthrough of the Snipara runtime loop: task intake, retrieval, shared context, project context, gap detection, drafting, memory, and when reindex or freshness checks matter.
Alex Lopez
Founder, Snipara
- Readable in 8 minutes
- Published 2026-04-27
- 6 context themes covered
Connecting an LLM to Snipara is not magic. A useful run follows a specific loop: identify the task, retrieve the right shared and project context, detect what is missing, draft with source priority, and only persist what is actually durable.
Key Takeaways
- Retrieval comes first — the model should not draft before it grounds itself.
- Shared context and project context play different roles — rules versus current truth.
- Memory is selective — store durable outcomes, not every prompt trace.
- Health and reindex matter — stale inputs produce bad answers.
The Runtime Loop
The user asks for a proposal section, a code fix, an architecture summary, or another concrete outcome.
Snipara identifies the active project and the relevant team-level context collections that apply.
The agent queries project docs, business or code shared context, memory, and optionally structural tools like code graph or runtime helpers.
If key facts are missing or stale, the agent should surface that instead of drafting with false certainty.
Only after retrieval does the model draft a section, propose a plan, or produce an implementation path.
If a real decision or validated learning was produced, it can be stored as reviewed memory for later runs.
Example: Business Task
Suppose the user asks: “Prepare the technical approach for the XYZ RFP.” A good agent flow is:
- load the active XYZ client project;
- load the Business Response Playbook and relevant business collections;
- pull current RFP material, discovery notes, and diagrams;
- look for similar historical examples without treating them as current truth;
- draft the technical approach with explicit gaps and assumptions.
Example: Code Task
Suppose the user asks: “Fix the auth regression in the billing checkout flow.” A good agent flow is:
- load the active code project;
- pull Team Code Context and relevant project docs;
- use code retrieval and graph tools to find the auth and checkout boundaries;
- identify tests, migrations, or API contracts at risk;
- only then propose or implement the patch.
What Should Become Memory
Memory should only keep information that is still useful next time.
Good candidates
- validated decisions
- stable repo or client preferences
- troubleshooting learnings that were confirmed
- reviewed patterns that should recur
Bad candidates
- raw prompt text
- unverified guesses
- whole documents already indexed elsewhere
- temporary noise from one session