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In the rush to experiment, one particular flavor of AI is rising fast across all industries: Agentic AI. And yes, that includes streaming. It’s capturing attention for good reason, but also sparking confusion. Depending on where you sit in the ecosystem, AI either feels like a golden ticket or a black box of promises.
So how can you get ready for a successful deployment in your streaming service? What key considerations do you need to think of beforehand? This blog unpacks those questions with honest thoughts from the field and a practical look at where this technology can drive real value.
There’s a common misconception that Agentic AI is about mimicking human-like intelligence in isolated tasks. But in practice, its true power lies in delegation and collaboration.
Agentic AI refers to autonomous systems that can respond to change, decide on a course of action, and work together in multi-agent environments to complete tasks with minimal human prompting. Unlike traditional generative models that produce new outputs from well-defined inputs and patterns, agentic systems behave more like collaborators. They retain awareness of context, interpret ongoing inputs, and improve through feedback. In essence, each agent is like a microservice with reasoning capabilities.
This model opens up a powerful new way to automate operations. But for many media companies and telcos, the idea of giving agents this level of autonomy raises valid concerns. Will the system make the right calls? What are the risks if it doesn’t?
Those are the right questions to ask.
To ground the conversation, it’s helpful to look at how Agentic AI is already being applied in other industries.
In e-commerce, agents are optimizing dynamic pricing based on user behavior, inventory levels, and competitor trends without explicit instructions. In logistics, they’re replanning delivery routes, reacting to traffic, delays, or even weather. In customer service, they’re resolving complex queries across multiple systems, escalating only when necessary.
What unites these use cases is the complexity of the environments they operate in. Each relies on real-time context, autonomy, and constant feedback. So in that sense, they’re not so different from streaming.
If you run a video platform, you’re already juggling hundreds of micro-decisions every day.
Which content goes on the homepage? What’s the right price point for this new tier? Why is engagement dipping in this region? Is the latest feature being well received? Agentic AI can help here, but deploying it successfully takes more than plugging in a new service like a turnkey API. It has to be shaped around your workflows, daily operations, and an underlying composable and modular platform that agents can interact with. Without that foundation,it will struggle to deliver on its promise.
You don’t just install an agent and walk away. A structured rollout matters. These five steps reflect what’s emerging as common practice when deploying Agentic AI across real-world, end-to-end platforms:
Start by identifying the problems Agentic AI can help solve—whether it's video quality issues, UI personalization, or operational friction. Assess your existing stack, your data readiness (we’ve tackled how to build a solid data foundation in a previous blog), and your internal skill set, while setting your expectations realistically. Map out the ROI over a 3–5 year horizon and make sure the initiative aligns with your wider automation or AI strategy.
Your system needs to support inter-agent collaboration. A single agent working in isolation may provide some benefit, but meaningful impact comes from fleets of agents working in sync. This includes:
• MCP (Model Context Protocol): Enables agents to access external data sources, tools, and services through standardized interfaces.
• A2A (Agent-to-Agent) protocols: Facilitates direct communication and task coordination between agents.
• Traditional protocols: REST APIs, WebSockets, etc. for integration with existing systems.
This multi-protocol approach ensures agents can both access necessary resources and coordinate with each other to complete complex workflows.
Successful multi-agent collaboration relies on proven orchestration patterns. We've observed six key approaches from actual deployments:
• Sequential Flow: For linear dependencies like content quality assurance pipelines.
• Parallel Processing: Simultaneously analyzing multiple dimensions of user experience.
• Conditional Branching: Triggering appropriate response workflows based on different issue types.
• Feedback Loop: Continuously improving recommendation algorithms and personalization.
• Human-in-the-Loop: Maintaining human oversight at critical decision points.
• Escalation Cascade: Layered problem resolution, from simple to complex.
Choosing the right orchestration pattern matters more than simply adding more agents. Each pattern has its optimal business scenarios and technical requirements.
Build CI/CD pipelines that don’t just deploy code, but also update context, run policy checks, and test agents before they go live. “Shadow mode” is a common first step: letting agents make decisions behind the scenes, without yet acting on them. Also, zero trust principles are critical to ensure that AI agents are constantly evaluated and restricted to prevent malicious actions or unauthorized access to resources. That way, you can track behavior safely before handing over control.
From KPIs to agent logs, traceability matters. Whether you’re dealing with internal compliance or cross-market regulation, you’ll want clear visibility into how decisions are made. Some teams even deploy observer agents to monitor agent performance in real time.
Find a low-risk, high-impact use case. Launch it in a controlled segment to test, measure and iterate. Only then should you think about scaling. Prove value and build trust first, both with your systems and your teams.
Agent autonomy isn't an "all or nothing" choice. From our work, we've mapped out four different autonomy levels:
• Autonomous: Agents operate independently, suitable for low-risk, repetitive tasks.
• Supervised: Agents execute but require human oversight and approval for critical decisions.
• Collaborative: Human-AI collaboration on complex tasks, each leveraging their strengths.
• Advisory: Agents provide recommendations and analysis but humans make final decisions.
The key is selecting the right autonomy level for different use cases. You don't need to grant agents full autonomy from day one. Start with Advisory or Supervised modes, then gradually increase autonomy as the system proves its reliability and your team's confidence grows. It’s a pragmatic way to stay in control while scaling up operational impact.
Each of these levels will also require the right support systems, such as monitoring, logging, and fallback mechanisms. At the end of the day, you’re building for unpredictability and you need to keep autonomy in check when conditions change.
Looking forward, Agentic AI is about creating platforms that are self-improving with intent. It’s a step change from rule-based or predictive AI that will separate the platforms that scale from those that stall.
If there’s one piece of advice we’d offer streaming leaders today, it’s this: treat Agentic AI not as a feature to add, but a capability to build. You’re building a new layer of operational intelligence on top of your end-to-end platform. Teams that treat it with the right guardrails and mindset will be the ones shaping the next phase of the industry.
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