Agentic Workflow

Limits of the single agent

An AI Agent is a computer system that combines LLM/LRM, tools, memory, context, and rules to observe, reason, and act. This figure of the “solo agent” has demonstrated remarkable capabilities, but also structural limitations. Even the most advanced models remain prone to bias, hallucinations, contextual forgetting, and instruction drift. These phenomena are not isolated anomalies but predictable effects of the statistical architecture of the models.

Thus, the use of a single agent faces a major constraint: it does not guarantee sufficient reliability for environments where accuracy, compliance, or traceability are essential.

Principles of the agentic workflow

The agentic workflow addresses these limitations by replacing the soloist with a distributed organization of specialized agents. Each agent is assigned a precise function within a defined flow: analysis, information retrieval, verification, writing, quality control, etc.

The goal is not to multiply models but to structure the work into distinct steps. Each step has a clearly delimited role, with defined inputs and outputs, and non-negotiable quality rules. Most importantly, each output can be verified, and the flow can be relaunched in case of error, bias, or hallucination.

This architecture introduces three major advantages:

1. Readability – each step is understandable and auditable.
2. Reliability – any result can be verified, corrected, or rejected before moving on to the next step.
3. Governance – the process becomes explainable, traceable, and adjustable.

Anatomy of a specialized agent

An agent in a workflow does not operate autonomously but within a controlled framework:

1. It is executed by an orchestrator that triggers its mission at the right time and with the right context.
2. Its output is checked automatically or by another agent, with the possibility of correction or relaunch.
3. It follows a clear input/output contract: expected format, mandatory fields, quality rules.

Example: validation of a sensitive press release.

• The communications agent produces a first draft of the text.
• The legal agent checks compliance: mandatory mentions, absence of misleading claims, compliance with sector regulations.
• The internal compliance agent ensures that tone and content respect the organization’s policies.
• Finally, a QA agent reviews the output: if an inconsistency or bias is detected, the response is sent back to the relevant agent for correction.

This flow illustrates how each agent is not a free entity but a controlled building block whose output is validated, corrected, or replayed if necessary.

Role of the orchestrator

To transform this collection of agents into an operational system, an orchestrator is indispensable. It has four functions:

1. Coordination – assign the right task to the right agent at the right time.
2. Flow control – ensure that an agent’s output meets the contract expected by the next agent.
3. Monitoring – log the steps, trace errors, measure performance.
4. Escalation – decide when to reintegrate a human operator into the loop.

The orchestrator is not meant to solve the tasks: it enforces the discipline of the process. It is both the backbone and the safeguard of the system.

Illustration: handling a customer ticket

Let’s take a concrete case: a customer reports a double charge on their subscription.

In a single-agent architecture, the response may be approximate and subject to interpretative bias.
In an agentic workflow:

1. Classification: identify the request as a billing issue.
2. Customer analysis: retrieve history, status, contractual commitments.
3. Precedent search: find similar cases previously encountered.
4. Product expert: validate the technical cause and applicable fix.
5. Legal agent: check legal compliance (mandatory mentions, refund deadlines).
6. Internal compliance agent: verify alignment with organizational policies.
7. Customer relations (Conflicts): draft a response in the appropriate tone, firm yet empathetic.
8. Final writing: consolidate everything into a coherent and actionable message.
9. Quality control: final check. If an omission or inconsistency is detected, the orchestrator relaunches the corresponding step.

The orchestrator executes the flow, logs everything, and triggers the necessary follow-ups. This process greatly reduces the risk of error, improvisation, or unrealistic promises.

Why this model is necessary

The agentic workflow is not just a stylistic choice: it is an industrial AI architecture, designed for reliability, governance, and performance.

• Resilience and controlled recovery: if an agent fails, the orchestrator can relaunch the task, redirect to an alternative agent, or escalate to a human. The system avoids global blocking but still ensures that critical steps are reprocessed.
• Modularity and scalability: each agent can be added, removed, or updated independently without disrupting the overall system.
• Reliability through cross-checking: agents review each other’s work or are monitored by a QA agent, reducing hallucinations by up to 89%.
• Parallel execution and adaptability: agents work in parallel and adjust the flow in real time based on feedback or workload.
• Collective intelligence: coordinating specialized agents generates richer performance than a single generalist agent.
• Enhanced traceability and governance: every decision is auditable, and layers such as Governance-as-a-Service ensure continuous regulation.
• Tangible results: adoption in finance, supply chain, and R&D with strong gains (efficiency, reliability, personalization).

In practice, the agentic workflow bridges the gap between the single agent, often brilliant but fragile, and the orchestrated AI ecosystem, robust, agile, and governable. It directly addresses the needs of enterprises in demanding professional contexts: compliance, service continuity, cost optimization, and reduction of operational risk.