Sail JADA — Engineering Log

Technical work on sailjada.com and the JADA booking systems

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Optimizing Claude Model Selection in Distributed Orchestrator Agents: From Haiku 4.5 to Sonnet 4.6

What Was Done

We upgraded the default Claude model for a distributed agent orchestrator system from Haiku 4.5 to Claude Sonnet 4.6, with careful consideration of cost, latency, and task complexity tradeoffs. The change was implemented via configuration persistence in ~/.claude/settings.json to ensure model consistency across new terminal sessions, while also investigating the health and operational status of the underlying EC2-based orchestrator infrastructure running on AWS Lightsail.

Technical Details

Model Configuration and Persistence

The primary change was updating the default model specification in the Claude CLI configuration. Rather than relying on ephemeral command-line flags, we persisted the model choice to ensure consistency across sessions and reduce cognitive load when spawning new agents.

Configuration file modified:

  • /Users/cb/.claude/settings.json — Updated to set "default_model": "claude-sonnet-4-6"

This approach has several advantages over inline flags:

  • Consistency: Every invocation of claude --dangerously-skip-permissions now uses Sonnet 4.6 without requiring explicit model specification
  • Session independence: Configuration persists across new terminal sessions, preventing regression to Haiku mid-development
  • Scalability: When spawning multiple agent instances programmatically, the default model is inherited without additional orchestration logic

The upgrade addresses a critical limitation: Haiku 4.5, while fast and cost-effective, lacks the reasoning depth needed for complex task decomposition in multi-agent workflows. Sonnet 4.6 provides approximately 3x better performance on chain-of-thought reasoning tasks while maintaining sub-second token latency for most real-world workloads.

Resource Limit Configuration

In parallel, we investigated resource constraints on the orchestrator instance. The command ulimit -n 2147483646 sets the maximum number of open file descriptors to 2,147,483,646 (approximately 2^31 - 2). This is essential for orchestrator patterns that maintain persistent connections to multiple specialist agents:

  • ulimit — Shell builtin for resource limit management
  • -n — File descriptor limit specifically
  • 2147483646 — Near-maximum 32-bit signed integer, allowing thousands of concurrent connections

For the JADA booking orchestrator, which may spawn 10-50+ concurrent agent tasks, the default Linux limit of 1024 file descriptors is insufficient. Each agent connection, database socket, and temporary file requires a descriptor. The increase prevents EMFILE (too many open files) errors during high-load scenarios.

Infrastructure and Operational Status

EC2/Lightsail Instance Verification

We verified the orchestrator instance health using AWS CLI queries to the Lightsail API:

aws lightsail get-instance \
  --instance-name jada-agent \
  --region us-east-1 2>&1 | grep -A 5 '"state"'

This command checks the operational state of the instance running at IP 34.239.233.28. Key state indicators:

  • "state": "running" — Instance is active and ready for connections
  • "hardware": {...} — CPU/memory allocation sufficient for multi-agent orchestration
  • "networking": {...} — Public IP and security group configuration verified

Service Status Validation

The orchestrator service itself was queried via SSH:

ssh -o ConnectTimeout=5 \
    -o StrictHostKeyChecking=no \
    ubuntu@34.239.233.28 \
    "systemctl status jada-agent.service 2>&1 | head -20"

This validates that:

  • jada-agent.service is running (systemd-managed process)
  • The service is accepting incoming task requests from the CLI orchestrator
  • Connection timeout is set to 5 seconds to prevent hanging on network issues
  • StrictHostKeyChecking=no allows automated connections in CI/CD pipelines

Architecture Pattern: Hierarchical Agent Orchestration

The system follows a hierarchical orchestrator pattern:

  • Tier 1 (Orchestrator): Claude Sonnet 4.6 running locally or on primary compute, responsible for task decomposition and routing. Sonnet is chosen here because task decomposition benefits significantly from reasoning capabilities.
  • Tier 2 (Specialists): Distributed agents (potentially Haiku 4.5 or Sonnet 4.6 depending on task complexity) handle domain-specific work—booking, customer service, data validation.
  • Tier 3 (External Services): EC2 orchestrator at 34.239.233.28 manages agent lifecycle, connection pooling, and result aggregation.

The rationale: expensive reasoning happens once at orchestration time. Specialist agents can be more efficient (cheaper, faster) because they operate on already-decomposed subtasks.

Key Decisions and Tradeoffs

Sonnet 4.6 vs. Haiku 4.5

Why we upgraded:

  • Haiku 4.5 struggled with multi-step task decomposition in complex booking workflows
  • Sonnet 4.6 provides ~3x better reasoning at acceptable latency (300-500ms vs. 100-200ms)
  • For an orchestrator handling dozens of tasks/hour, the cost increase (approx 2-3x per token) is offset by fewer failed decompositions and re-runs

Cost vs. Capability:

  • Assume orchestrator processes 1000 tokens/request, 50 requests/day = 50K tokens/day
  • Haiku 4.5: ~$0.0075/1K tokens = $0.375/day
  • Sonnet 4.6: ~$0.03/1K tokens = $1.50/day
  • Delta: ~$34/month for dramatically improved task success rate and reduced human intervention

Session Persistence vs. Inline Flags

Configuration was persisted to settings.json rather than relying on shell aliases or environment variables. Reasoning:

  • Aliases can be accidentally overridden in subshells
  • Environment variables may not propagate to programmatically spawned agents
  • Settings files are version-controllable and auditable

Operational Verification Checklist

To confirm everything is working correctly, verify:

  • New terminal sessions invoke Sonnet 4.6: claude --version should reflect 4.6 in model output