Agent Context Isolation
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Parallel Agent Orchestration
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See how to installReview the instructions the agent will receive before you install.
When launching multiple agents in parallel, follow this pattern to avoid context bloat.
run_in_background: trueFor tasks where agents just need to confirm completion:
# Agent writes to shared status file
echo "COMPLETE: <task-name> - $(date)" >> .claude/cache/<batch-name>-status.txt
>> to append (not > which overwrites)cat .claude/cache/<batch-name>-status.txtFor tasks requiring detailed findings:
.claude/cache/agents/<task-type>/<agent-id>/
├── output.md # Main findings
├── artifacts/ # Any generated files
└── status.txt # Completion confirmation
# Task: <TASK_NAME>
## Your Mission
<clear objective>
## Output
When done, write confirmation:
\`\`\`bash
echo "COMPLETE: <identifier> - $(date)" >> .claude/cache/<batch>-status.txt
\`\`\`
Do NOT return large output. Complete work silently.
// Launch all in single message block (parallel)
Task({
description: "Task 1",
prompt: "...",
subagent_type: "general-purpose",
run_in_background: true
})
Task({
description: "Task 2",
prompt: "...",
subagent_type: "general-purpose",
run_in_background: true
})
// ... up to 15 parallel agents
# Check completion status
cat .claude/cache/<batch>-status.txt
# Count completions
wc -l .claude/cache/<batch>-status.txt
# Watch for updates
tail -f .claude/cache/<batch>-status.txt
run_in_background: true always>>) not overwrite (>)# Status file
.claude/cache/provider-backfill-status.txt
# Each agent appends on completion
echo "COMPLETE: anthropic - Thu Jan 2 12:34:56 2025" >> .claude/cache/provider-backfill-status.txt
echo "COMPLETE: openai - Thu Jan 2 12:35:12 2025" >> .claude/cache/provider-backfill-status.txt
Check progress:
cat .claude/cache/provider-backfill-status.txt
# COMPLETE: anthropic - Thu Jan 2 12:34:56 2025
# COMPLETE: openai - Thu Jan 2 12:35:12 2025
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Agent Context Isolation
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Agent Orchestration Rules
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Agentic Workflow Pattern
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Guide for integrating Agentica SDK with Claude Code CLI proxy
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Reference guide for Agentica multi-agent infrastructure APIs
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Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity
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