Build, discover, and monetize AI agents.

Writer sharpens agentic AI with Palmyra X6 and cheaper long-running agents

What changed: Writer released its Palmyra X6 flagship model alongside major upgrades to its AI agent platform for marketing and revenue teams. Agents paired with X6 now run complex multistep workflows at an average of 52% lower cost, 48% faster speed, and 10% better quality, with tasks completing in about 26 seconds at 82 tokens per second and able to work unattended toward goals for up to eight hours.

Why it matters: Cheaper, faster long-running agents make it practical to automate campaign execution, testing, and reporting end to end, rather than relying on single-step assistants.

Try/watch: Start by moving one high-volume, repetitive revenue workflow—such as email sequence optimization or ad creative testing—onto X6-powered agents and use the enhanced reporting and governance to track savings and risks.

Google’s Gemini 3.7 Flash cuts costs for coding and agent tasks

What changed: Google released Gemini 3.7 Flash, a targeted AI model optimized for software development, agent tasks, and document processing at roughly half the launch price of Gemini 3.6 Flash. The model offers a context window of up to 1 million tokens, a maximum output of 64,000 tokens, and introductory pricing of $0.75 per million input tokens and $3.75 per million output tokens through the end of 2026, with access via Google AI Studio, Android Studio, Antigravity, enterprise agent platforms, and Gemini Spark for subscribers.

Why it matters: Lower prices and larger context windows make it easier for teams to build agents that operate over full codebases, knowledge bases, and long-running workflows without blowing up infrastructure budgets.

Try/watch: Builders should benchmark Gemini 3.7 Flash against their current model on a real agent workload—such as repo-level coding assistance or document-heavy customer-support flows—to see if the lower cost and larger context justify a switch.

FriskAI launches runtime intelligence for monitoring what agents actually do

What changed: FriskAI Inc. launched with $3.6 million in pre-seed funding to give enterprises a detailed record of what AI agents do once they are in production. The startup positions runtime intelligence as a way to capture and analyze agent behavior across live systems, closing the visibility gap between development-time tests and real-world deployment.

Why it matters: As agents gain more autonomy, leaders need audit trails and behavioral analytics to satisfy compliance teams, investigate incidents, and decide whether to expand or roll back agent permissions.

Try/watch: If agents already touch customer data or financial systems, pilot a runtime-intelligence tool in one environment, define clear alert thresholds for unexpected actions, and use the logs to refine both prompts and access controls.

Korean manufacturers pivot from in-house chatbots to top-tier AI agents

What changed: Reporting from Korea indicates manufacturers are moving away from internally built chatbots and toward top-tier general-purpose AI agents that can handle coding, verification, and program execution, as performance gaps have become too large to ignore. The U.K. National Cyber Security Centre has advised organizations adopting agentic AI to apply least-privilege access, limit the scope of agent actions, monitor for anomalies, and start with repetitive, low-risk tasks.

Why it matters: The shift suggests that for many industrial teams, it is now more effective to integrate frontier agent platforms with strong safety guidance than to invest heavily in bespoke assistants that lag in capability.

Try/watch: Manufacturing and engineering leaders should map a small set of low-risk, repetitive tasks—such as report generation or test scheduling—to external agents and implement least-privilege access and anomaly monitoring from day one, following NCSC-style recommendations.