What's the biggest benefit over a single large LLM prompt?

Granular control, better reliability, and cost efficiency. Specialized agents excel at their narrow tasks, reducing hallucination and improving consistency compared to a monolithic prompt trying to do everything.

How do you manage state between agents?

We use a central data store, typically Firebase, to persist context and outputs. Each agent reads its required context and writes its results back, ensuring a consistent source of truth for the entire workflow.

Is human-in-the-loop always necessary?

For critical or creative tasks, yes. For routine, low-risk operations, it can be minimized or removed. Our dashboard allows us to dial this in per workflow, balancing autonomy with necessary control.

Concept · agents · in production

AI Agent Orchestration

We use AI agent orchestration to manage complex, multi-step workflows by assigning scoped roles to individual agents, facilitating hand-offs, and maintaining human oversight via a central dashboard.

What it is

AI agent orchestration moves beyond single, monolithic LLM prompts to a system of specialized, interconnected agents. Each agent is designed with a specific role, a tailored context window, and access to a defined set of tools. The core idea is to break down a complex task into a series of smaller, manageable sub-tasks, with each sub-task handled by an agent best suited for it. This approach mirrors a microservices architecture, where individual components communicate and hand off work in a structured manner. A central orchestrator, often an event-driven daemon like our VERA, manages the workflow, dispatches tasks, and monitors progress, ensuring a cohesive output from the distributed intelligence.

Why it matters

This method offers several advantages over attempting to solve large problems with a single, massive prompt. It significantly improves reliability by isolating failures; if one agent falters, the system can often retry or route around it without collapsing the entire workflow. Cost efficiency is another key driver: specialized agents can leverage smaller, more focused models (e.g., a specific Claude Code instance for code generation, a lighter model for text summarization) rather than requiring a large, general-purpose model for every step. This modularity also enhances control, allowing for precise human intervention at critical hand-off points, which is essential for maintaining quality and alignment with our product vision as we are building in public.

How TV applies it

At Total Ventures, our agent orchestration system is built around VERA, our internal operations daemon. VERA acts as the primary orchestrator, monitoring events from Firebase and dispatching tasks to various agents. For instance, when a new feature idea is logged, VERA might trigger a planning agent to draft user stories, then hand off to a Claude Code agent for initial code scaffolding. This generated code is then pushed to a staging environment, and a human-in-the-loop dashboard allows for review and approval before deployment via Vercel. For content generation, a similar flow applies: a content brief triggers a drafting agent, followed by a refinement agent, and finally an email agent (using Resend) for distribution. Firebase serves as our central state management layer, ensuring context is consistently passed and persisted between agents.

Common failure modes

Implementing agent orchestration isn't without its challenges. One common pitfall is ambiguous agent roles, leading to overlap, conflicts, or gaps in the workflow. If agents aren't clearly delineated, they can step on each other's toes or miss crucial steps entirely. Poor hand-off protocols are another frequent issue; if context or data isn't correctly formatted or passed, subsequent agents will struggle. A lack of effective human oversight can lead to agents running unchecked, producing undesirable or costly outputs. We've also observed over-orchestration: creating too many agents for simple tasks, which introduces unnecessary complexity and overhead without proportional benefit. Finally, inconsistent state management across agent interactions can result in data integrity issues or agents working with outdated information.

FAQs

What's the biggest benefit over a single large LLM prompt?: Granular control, better reliability, and cost efficiency. Specialized agents excel at their narrow tasks, reducing hallucination and improving consistency compared to a monolithic prompt trying to do everything.
How do you manage state between agents?: We use a central data store, typically Firebase, to persist context and outputs. Each agent reads its required context and writes its results back, ensuring a consistent source of truth for the entire workflow.
Is human-in-the-loop always necessary?: For critical or creative tasks, yes. For routine, low-risk operations, it can be minimized or removed. Our dashboard allows us to dial this in per workflow, balancing autonomy with necessary control.

Want to see how Total Ventures applies this in production?

See the brand portfolio →