AI coding agents can work effectively within a session, but what happens when that session ends?
What if another developer needs to continue the work? What if you want to switch from Claude Code to Codex? What if one agent investigates a bug and another implements the fix?
The biggest risk is losing the reasoning behind the work.
The context handoff problem
Repository access gives an agent access to code. It doesn't necessarily explain:
Why a particular implementation was chosen.
Which approaches were already tested.
What constraints must be respected.
What remains unfinished.
What the next agent should do.
Five practices that help
- Keep reusable project instructions
Use project-level instruction files for architecture, coding conventions, and shared rules.
- Record task-specific decisions
Keep implementation choices, relevant files, and investigation findings somewhere accessible.
- Maintain a progress summary
Record completed work, unresolved issues, and the next steps before ending a session.
- Use structured handoffs
Give the receiving agent a clear objective, constraints, previous findings, and expected output.
- Keep the team involved
Make task history and review feedback accessible to teammates.
Where Sharkly.ai fits
Sharkly.ai connects shared Tasks with human assignments, Agent or Crew execution, comments, activity, and run history.
This allows the team to keep important task information in a shared workspace and assign the same work to another Agent or teammate when needed.
It doesn't mean private Agent conversations are automatically transferred. The important context must be recorded in the shared Task.
Explore: https://sharkly.ai/features/tasks/
Documentation: https://help.sharkly.ai/docs/space/tasks
How do you currently preserve context across AI coding sessions?
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