Avoid the Pitfalls: How to Ensure Your AI Project Beats the Odds
The term “Agentic AI” is everywhere. Microsoft talked about it at Ignite 2025. IDC reports 37% of organisations already using it. But if you work in media operations (running a gallery, managing post workflows, coordinating live sports, keeping digital distribution online) you have one question: what does this actually do?
This article explains what agentic AI means in practice for media operations and why it matters now.
What Makes AI “Agentic”?
Agentic AI = AI systems where agents can plan, reason, act, and learn, with human oversight.
The key difference from generative AI? Autonomy. ChatGPT responds to your prompt and stops. An agent gets a goal (ensure tonight’s deliverables meet QC spec and are in the right place by 18:00) and works through multiple steps to achieve it. You set the goal and guardrails. The agent figures out the path.
Why Now? Three Capabilities Converged
1. LLMs reason across unstructured information
Modern models understand context, interpret ambiguous situations, generate plans. Agents now work with media’s messy reality: incomplete metadata, PDF call sheets, inconsistent file naming, Teams messages.
2. APIs matured
Your MAM, traffic system, rights database, CRM, cloud storage all have APIs. Agents can update records, trigger workflows, send notifications, move files.
3. Organisations have data strategies
You don’t need perfect data, but you need some structure. Programme IDs, standardised rights codes, structured schedules, defined delivery specs. Agents work with “good enough” data and surface inconsistencies for improvement.
What Agents Do: Real Workflow Examples
Intelligent Delivery QC and Routing
The old way: Delivery coordinator manually checks files against spec sheets. Something wrong? Email post-production. Wait. Get revised file. Check again. Manually upload to broadcaster portal. Update traffic system.
With agents: Agent monitors delivery queue, runs technical QC against spec, checks metadata completeness.
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Issues found? Structured report to the right person via Teams, monitors for corrections.
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File passes? Uploads to broadcaster portal, updates traffic system, logs in MAM. Delivery window at risk? Escalates to human with full context.
Impact: faster delivery, reduction in failed QC. Coordinators focus on genuine exceptions, not repetitive checking.
Live Sports Monitoring and Incident Response
The old way: Operations teams watch multiple dashboards during live events. Bitrate drops. Someone notices, correlates logs from multiple systems, identifies cause, decides fix, executes, verifies. By then, viewers have churned.
With agents: Agent monitors telemetry from encoders, CDN edges, player logs. Detects bitrate anomaly, cross-references logs, identifies failing encoder, attempts automated mitigation (switches to backup). Confirms resolution, logs incident. Mitigation fails? Escalates with all context gathered.
Impact: faster incident detection, reduction in viewer-impacting outages. Operations teams make strategic decisions instead of log-hunting.
Archive Content Discovery
The old way: Researcher needs “wide shot of Thames at sunset, 1990s, London Eye not yet visible.” Spends hours searching by keyword, often gives up, commissions new footage.
With agents: Researcher describes need in natural language. Agent translates request into structured search across visual content, metadata, AI-generated scene descriptions. Filters by era, identifies candidates via visual analysis, presents shortlist. Researcher selects? Agent checks rights, generates licensing request, adds clip to project bin.
Impact: reduction in search time, increase in archive reuse, fewer commissioned shoots.
Where Agents Live: The Control Layer
Agents need identity, data access, compute, orchestration, oversight. For most media organisations, that’s the Microsoft platform you already own (Entra ID, Microsoft 365, Azure).
The critical piece is a normalised data layer that brings together content, metadata, rights, schedules from your MAM, traffic system, CRM, rights database. Without this, agents can’t understand context or make decisions grounded in your reality.
This is where platforms like AIR Fusion from Support Partners matter. They provide the media-native intelligence layer that sits between your agents and your various systems, giving agents a consistent view of programmes, series, episodes, rights windows, delivery specs regardless of which vendor systems you use.
The Multi-Cloud Reality
Here’s what matters if your media lives in AWS, Google Cloud, or on-premises: you don’t need to migrate content to use AI agents.
A normalised intelligence layer can sit across your infrastructure:
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Connect to AWS S3, pull metadata, trigger MediaConvert jobs
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Federate access to Google Cloud Storage
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Reach into on-prem systems (Avid, Dalet, Imagine) via secure connectors
Agents query the intelligence layer, which maintains a logical view of content, rights, metadata regardless of where files physically sit. The file never moves. Only metadata, decisions, orchestration flow through the control plane.
Why this matters: A broadcaster with their entire post pipeline on AWS (S3 ingest, MediaConvert transcode, CloudFront delivery) can deploy agents that authenticate via Entra ID, coordinate via Teams, reason over unified content models, and translate actions into AWS API calls. Agentic AI plus Microsoft governance without rebuilding infrastructure.
The Hidden Cost: Egress
Agents access the same content multiple times (analyse, extract clips, generate marketing, run compliance). Each access = fresh egress charge. AI projects can become economically unviable.
The solution is intelligent caching with AIR Fusion:
- Extract/enrich metadata once. Intelligence lives in the control plane. Agents reason over metadata without accessing source files. No egress cost.
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Cache lightweight proxies for frequently accessed content. Review using proxy. Only retrieve high-res master when committing to use.
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Monitor access patterns. High-demand content? Replicate. Cold content? Stays in original location with only metadata in control plane.
Real example: Broadcaster with 800TB archive in AWS exploring AI enrichment. Initial estimate: $120,000 egress fees. With AIR Fusion intelligent caching: $15,000 (90% reduction).
Managing Key Risks
Bad Data: Agents amplify metadata problems. Start with reasonable data quality workflows. Use agents to surface inconsistencies for fixing.
Over-Automation: Remove all human judgment, system becomes fragile. Design agents to escalate edge cases to humans. Keep humans in loop at decision points that matter.
Compliance: Agent makes mistake? Need to trace what happened. Build observability and logging into workflows from day one. Every agent action should be auditable.
Multi-Cloud Complexity: Agents understanding AWS, Azure, Google Cloud APIs individually creates fragile code. Normalised abstraction layer (what AIR Fusion provides) means agents interact with consistent media model, translation to each system handled underneath.
Where To Start
Crawl (Month 1-3): Pick one workflow (automated QC, live monitoring, transcription, file routing). Build agent for that task. Measure impact.
Walk (Month 4-6): Chain tasks into workflows. Ingest, QC, transcode, deliver, notify. Agents reason across steps, hand off to humans at right moments.
Run (Month 7-12): Deploy multi-agent systems. Promo agent plus archive agent plus delivery agent working end-to-end. This is where Frontier firms (2.84x AI ROI according to IDC) operate.
Why Now Matters
IDC research: only 6% of media organisations qualify as “Frontier firms” for AI. Lowest of any industry. Genuine first-mover advantage exists for organisations acting now.
Organisations industrialising agentic AI over the next 12 to 24 months (on platforms they own, grounded in normalised data layers, without migrating infrastructure) will pull ahead. They will deliver content faster, at lower cost, with fewer errors.
Organisations waiting for “perfect” point solutions or thinking they need one-cloud migration first? They fall behind.
The Bottom Line
Agentic AI isn’t hype. It’s practical capability ready now.
It isn’t about replacing people, its about giving them digital crew handling repetitive, multi-step, cross-system work.
Its not about buying another stack but extending platforms you have with media-native intelligence.
And its not about migrating media but putting unified secure control plane on top of infrastructure you have.
The question: “If we could reduce delivery times, cut QC errors, and let people focus on creative decisions instead of data entry (across AWS, Azure, Google Cloud, on-premises), what would that be worth?”
That’s what agentic AI means for media operations.
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Ready To Start? Here’s Your Next Step
Book a Catalyst Discovery Workshop with Support Partners.
In one focused 90-minute session, we’ll conduct a pragmatic exploration of whether agentic AI can deliver value in your specific operations, on the platform you already own, without asking you to rip out infrastructure you’ve built.
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Map one high-impact workflow (delivery, live ops, promo, archive, your choice)
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Show where agents fit in your current Microsoft environment
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Demonstrate how AIR Fusion with Catalyst unifies your multi-cloud reality
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Design a 90-day proof-of-concept with measurable outcomes
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