Multi-Agent Content Orchestration

Maintaining output coherence and quality while coordinating multiple autonomous agents without manual supervision.

Single-prompt content generation breaks down as scope increases. Long-form writing, SEO alignment, citations, and multi-format outputs introduce competing goals that cannot be reliably handled by a single model invocation.

Naively chaining agents creates new problems: duplicated work, contradictory outputs, runaway loops, and gradual quality decay. Human review does not scale, so the system had to be designed to fail gracefully and self-correct.

The challenge was not generating content, but coordinating responsibility across agents.

The system focused on agent coordination and task boundaries, rather than prompt engineering.

This centered on:

• Explicit task decomposition with clearly scoped agent responsibilities
• Structured handoffs between agents to avoid context bleed
• Deterministic execution order with bounded retries
• Guardrails to prevent recursive or self-reinforcing outputs

Agents are treated as unreliable collaborators, not intelligent teammates.

Demonstrates the ability to design AI systems as distributed workflows rather than single interactions.

Shows an understanding of failure modes in agentic systems, the limits of model autonomy, and how to impose structure where intelligence alone is insufficient.