Build, discover, and monetize AI agents.

Enterprise advertisers start wiring core ad and service stacks to AI agents

What changed: Enterprise advertisers and customer-service teams are moving from pilots to full-stack deployments of agentic AI, with Warner Bros. Discovery rebuilding its ad tech stack on AWS agents, Yahoo launching an open agent network across 23 partners, and WPP testing AI buyer agents for video. A Salesforce survey in the same roundup reports service organizations’ agentic AI adoption rising from 39% to 66% in a year, with 70% seeing measurable returns within 60 days.

Why it matters: For go-to-market leaders, this signals that AI agents are now credible infrastructure, not side experiments, and competitors may soon automate planning, trafficking, and optimization loops end to end. Because agentic systems can plan multi-step workflows, use tools, and act autonomously within guardrails, they can compress cycle times and reduce the headcount needed for routine campaign work.

Try/watch: Audit one revenue-critical workflow (planning, trafficking, or reporting) and run a 60-day agent pilot with clear baselines on cycle time, error rates, and human hours so you have hard data before committing to a broader rebuild.

Symphonica brings agentic, no-code OSS to telecom operations

What changed: Symphonica introduced an agentic AI-powered operational support system that lets communications service providers and some enterprise customers design and adapt services using no-code and “vibe coding” chat interfaces, then orchestrate deterministic workflows over non-deterministic agents. The platform adds governance and full traceability around these agentic workflows so operators can see decisions and validate each flow before it runs in production.

Why it matters: Network and operations leaders can now let domain experts prototype new services and automations without waiting on overburdened IT teams, while still enforcing guardrails and approvals. This shortens time-to-market for offers and network workflows but increases the need for strong governance so experimental agents do not create unstable or noncompliant configurations.

Try/watch: Identify one narrow but high-value OSS or BSS workflow with frequent change requests, and test an agentic no-code approach there first to prove safety, governance, and ROI before exposing core network functions.

Reality check: only a small minority run fully autonomous agents in production

What changed: A new analysis reports that only about 7% of companies today run fully autonomous AI agents in production, underscoring how rare truly end-to-end automation still is despite heavy investment. Fresh practitioner guidance this week notes that many agentic AI projects are at risk of cancellation because teams bolt agents onto existing processes instead of redesigning workflows and fail to set clear baselines for time, error rate, and human effort.

Why it matters: Leaders are under pressure to “do agents,” but the gap between hype and live deployments means the real advantage goes to teams that treat agentic AI as business architecture work, not just another model integration. Thoughtful scoping, measurement, and guardrails also reduce the risk of overspending on pilots that cannot scale or failing compliance and reliability audits after deployment.

Try/watch: Before committing to a big agent roadmap, pick one workflow, document its current steps and KPIs, run a limited agent pilot with explicit go/no-go thresholds, and review results with risk, operations, and finance so future deployments start from a shared definition of value.