7 best practices for leading and managing agentic teams

Roles and responsibilities in agentic AI teams

The introduction of agentic AI forces us to rethink the roles in the enterprise. AI agents excel at scale, speed and consistency. AI agents can handle tasks such as the following:

• Processing large volumes of data.
• Operating continuously.
• Executing multistep workflows.
• Generating drafts.
• Identifying patterns.
• Coordinating routine decisions.

Strategic intent, goal definition, ethical judgment and accountability remain human responsibilities. Humans also handle ambiguity, edge cases and interpersonal dynamics, areas where AI agents will be out of their depth.

Managers of agentic teams are shifting from supervising tasks to supervising systems. Instead of reviewing every output, they define objectives, monitor performance trends, handle escalations and continuously refine guardrails. It's worth reiterating that accountability doesn't transfer to machines. Enterprises, and the humans who deploy AI, remain responsible for outcomes.

Best practices for managing agentic AI teams

Technical capabilities and management best practices drive the success of agentic AI initiatives. Operational managers must integrate agents into workflows, train teams and continuously monitor performance. Executives must set boundaries on what AI will not do. Strategy, ethical judgment, people management and final accountability remain human responsibilities.

1. Design human-AI collaboration models and workflows

Redesign processes to reflect this new division of labor. Decompose workflows into tasks and assign each to the appropriate actor: traditional automation, AI agents or humans. Decide where agents operate independently, where they recommend actions and where humans must approve them. Clear handoff points and feedback loops are essential. Humans must be able to correct agents easily, and those corrections must feed system improvement.

2. Define clear objectives and success metrics

Agentic AI systems are goal-driven, and unclear objectives yield unpredictable behavior. Effective deployments start with clear definitions of what success looks like. Objectives should be specific and measurable. Examples include reducing processing time, increasing throughput, improving consistency or freeing human capacity for higher-value work. Metrics must go beyond raw accuracy to include task success rates, error rates, escalation frequency and human override rates. Equally critical is measuring the performance of the entire human-AI system. Productivity gains, cycle-time reductions and quality outcomes matter more than isolated AI metrics.

3. Monitor agent performance and behavior

Agentic systems require continuous oversight. Managers need visibility into intermediate decisions, tool use, confidence levels and deviations from normal patterns. Treat agent monitoring like production operations: Have dashboards, alerts, sampling reviews and regular audits. Agents' performance can degrade, and without observability, errors accumulate unnoticed. User feedback is a critical signal, and systems should make it easy to flag questionable outputs for further analysis.

4. Manage errors and coordination failures

Enterprises should prepare to onboard and closely supervise their agents. Escalation paths must be explicit and fallback to humans graceful. Agents should know when to stop and ask for help. In multi-agent systems, coordination failures, loops and conflicts are common. Clear scopes, orchestrators and escalation rules reduce these risks. Regular post-mortems on AI incidents, treated with the same rigor as operational failures, can enhance organizational learning and help agentic AI scale.

5. Calibrate access controls and capture audit trails

Role-based access controls and least-privilege principles are mandatory. Agents should only access the data and systems they require for their role. Audit trails must capture what agents did, why and who directed them. Define tiers of autonomy aligned to the level of risk. Incorporate approval thresholds into systems rather than relying on informal norms.

6. Ensure ethical responsibility and harm prevention

Agentic AI can amplify ethical risk because it operates at high velocity and at scale. Bias, unfair outcomes and misuse can spread rapidly or become entrenched. Best practices include bias audits, transparency in AI-driven decisions and clear disclosure when AI is involved. Explainability and traceability are essential in high-stakes contexts. Enterprises must also plan for both intentional and accidental misuse. Security controls, resource limits and monitoring for anomalous behavior are part of responsible management.

7. Adapt organizational roles and operating models

Managing agentic AI requires new roles and capabilities. Many enterprises are establishing AI agent centers of excellence, identifying agent owners within business units and creating dedicated governance functions. Operating models must evolve to treat AI agents as managed assets, with lifecycle management, versioning and accountability like other critical systems. Employee training is important, and employees need to learn how to work with agents.

Case studies and real-world examples

A few examples illustrate how enterprises are using agentic AI teams.

In financial services, banks are deploying agentic systems to assist with credit risk analysis. The roles and responsibilities are divided based on respective strengths. Agents draft memos, extract data and flag anomalies, while human officers retain decision authority. Results include faster turnaround and improved consistency.

In the pharma sector, research teams are using AI agents as digital chemists to optimize research experiments. The agents monitor experimental data in real time and suggest optimal timing for actions such as solvent changes -- decisions traditionally based on human expertise. The agent operates within safety constraints encoded by domain experts and never executes actions autonomously. It only generates recommendations for scientists to review.

Global manufacturer Cosentino built AI customer support agents to handle routine inquiries across regions and languages. Built on a multi-agent architecture, the system handles FAQs, troubleshooting and service scheduling. Agents escalate more complex or emotional cases to human representatives.

Automobile company BMW implemented a multi-agent system to support supplier management and decision-making across its global operations. Individual agents specialize in scanning supplier data, quality reports, logistics signals and external events. A coordinating agent synthesizes insights in response to manager queries. The system enables procurement and operations teams to identify risks and opportunities faster than manual analysis. Managers can ask complex questions and receive synthesized, actionable responses within minutes.