Use Cases
Shared Context
How to make agentic AI multiplayer — knowledge that people and their agents retrieve, use and improve together, across tasks and conversations.
Each is researching, planning and building with an agent. But much of what they learn stays in separate conversations. A decision gets explained again. A correction never reaches the next person. An unanswered question gets rediscovered.
What if that work became a shared experience?
Shared context is knowledge that people and their agents can retrieve, use and improve together, across tasks and conversations.
It includes the facts, decisions, constraints and reasoning behind the work. What do we know? Why did we make this choice? What changed? What still needs an answer?
What becomes possible
Start where the knowledge gets tested: what AI says about you.
Someone asks an assistant a question a customer would ask, and the answer comes back with a detail that is not quite right — a price without the condition that ends it. Because the team keeps its approved facts in one place, that answer can be checked against them rather than argued about. The gap turns out to be real: nothing on file settles it. The person who owns that fact writes it down, and the correction is saved where the next question will find it.
That is the loop in miniature — ask, check, resolve, save, and use the improved context in the next task — and it is the same loop whatever the work is.
Take product planning. A product lead saves the requirements and the reasoning behind them. A developer's agent retrieves that plan before implementation and surfaces an unanswered question: what should happen to existing customers? The team resolves it and saves the decision. Another teammate's agent retrieves that clarification when preparing tests or writing release notes.
The same pattern supports several kinds of work:
- Verifying AI answers: Compare what a model says about your products, policies or prices against the facts you approved. Resolve what it cannot support, then ask again.
- Collaborative coding: Carry architecture decisions, constraints and debugging findings between developers and their agents. Check them against the current code before continuing.
- Research and planning: Build on sourced findings, preserve open questions and revise a plan as evidence changes.
- Content and customer answers: Generate a brief, explanation or response from approved information, then check its claims against those sources.
- Knowledge maintenance: Use real questions to discover missing information, outdated guidance and conflicting documents.
How to make shared context dependable
Sharing more information only helps if people can understand what to trust and what to change. A few practices make the difference.
Version the knowledge. Keep a history of important edits: what changed, who changed it and why. Use Git for text-based knowledge where it fits, or document revisions with comparable history. Tie generated outputs to the source versions they used, so later changes can trigger a review.
Resolve conflicts deliberately. Give important information an owner and a status. Keep drafts separate from approved decisions. When two contributors change the same guidance, review the differences rather than silently overwriting one version. Reduce duplication by updating the authoritative source and linking to it.
Analyze gaps through actual use. Ask questions the team needs answered. Inspect the retrieved evidence. Distinguish a missing source from a false claim, and an outdated fact from an unresolved decision. Retrieval can miss information that exists, so investigate before declaring the knowledge absent.
Generate, check and contribute back. Use the context to produce useful work. Check the result against its sources, resolve material omissions and save the reviewed contribution. Automatically feeding every generated answer back into the knowledge base risks accumulating errors.
Make access and handoffs explicit. People and agents should retrieve only what they are permitted to see. Save enough context for someone else to continue: the decision, supporting material, open issues and next step. A fresh session must retrieve an update; saving it does not refresh every active conversation.
Search, version history, comparisons, issue tracking, evaluations and generation tools all support this practice. Their value is in helping the team complete the loop above.
Keep organizational memory under your control
Working in silos leaves useful knowledge scattered across conversations and providers. A shared context layer gives the organization a durable home for that knowledge, independent of whichever model someone uses today.
That home can be local or on infrastructure the organization controls. Local storage still needs a way for authorized teammates to access and synchronize it, along with backups and conflict handling. Shared context is an architectural choice, not a guarantee that any particular tool stores everything locally.
The principle I care about is simple: keep the organizational memory under your control, and choose when to bring a model to the work.
A frontier model can help distill a long discussion, extract decisions, compare sources or propose a clearer explanation. Give it the relevant, permitted information for that task, review what it produces and preserve the useful result in your own context layer.
Local storage and local processing are different. Material sent to a hosted model leaves your local environment and is subject to that service's data handling. If information must stay entirely within your environment, the processing must stay there too.
This approach gives teams continuity across tools, more control over disclosure and less dependence on individual conversation histories.
From shared knowledge to better training and evaluation
Maintained context can also become raw material for improving models you train or adapt.
Reviewed questions and answers can become fine-tuning examples. Corrected failures can become evaluation cases. Accepted and rejected responses can inform preference datasets. These require deliberate selection, permission, quality checks and versioning. Keep evaluation cases separate from training examples, so you can measure performance on work the model has not already seen. Fine-tuning data practices
For reinforcement learning, organizational knowledge can help define realistic tasks, constraints and success criteria. A coding task might require an agent to implement a change while preserving documented behavior, with tests checking the result. Building the environment still requires executable actions, observable outcomes and a reward that measures success reliably; documents alone are not an RL environment. Environment-based training
Frontier models can help prepare these materials, but their outputs need checking. A model-generated example should earn its place in the dataset through evidence and evaluation.
Retrieval supplies knowledge during a task. Fine-tuning changes model behavior through training. Both can benefit from better organizational context, but each needs its own tests. Frequently changing facts should remain maintainable sources rather than relying solely on what a model learned during training.
What this changes for a team
The benefit goes beyond saving time on repeated explanations. Teams can preserve the reasoning behind decisions, catch disagreements earlier and reuse corrections across projects and agents. They can choose different models while continuing to build the same organizational memory.
Progress becomes tangible: a question the team could not resolve now has a supported answer. A handoff is clear enough for someone else to continue. A corrected failure becomes a test that helps catch it next time.
Your team's next task should benefit from what it just learned.
Next steps
- Verify AI answers — the loop above, step by step, with the commands.
- Start Here — set up a context layer your team and agents share.
- Why Senso — what makes an answer provable.