Introducing AgentFlow, the Agentic AI Platform for Financial Services In 2026, “using AI” in banking and insurance isn’t the hard part. Running AI safely inside real workflows is. Most teams start with point tools and pilots. Then reality hits: 1. workflows span documents + systems + approvals + exceptions 2. multiple agents need to collaborate reliably 3. compliance needs traceability, controls, and audit-ready logs scaling across teams turns into fragmentation and cost. That’s why we built AgentFlow: an all-in-one platform for creating, orchestrating, and monitoring AI agents in finance and insurance, with governance built in. Key takeaways 1. AgentFlow is an all-in-one platform for creating, orchestrating, and monitoring AI agents in finance and insurance. 2. It embeds governance with confidence scores, explainability, and audit trails for compliance and trust. 3. Flexible deployment and self-serve or white-glove setup let teams automate workflows at their own pace. #Productvideo# #MultimodalAgentFlow#

Despite the fintech surge, trust remains a credit union advantage. Consumers still see CFIs as safer than digital banks, especially when it comes to data privacy and fraud protection. Double down on that perception. Use AgentFlow to operationalize safety: 1. Employ Decision AI for proactive fraud detection 2. Automate suspicious activity alerts via Conversational AILog and audit every action for compliance with full traceability Security and governance are embedded in the AgentFlow platform, from SOC 2 Type II compliance to VPC deployments and immutable audit logs.

Big news! Multimodal has been selected as the build partner for FiLab’s Agentic AI Discovery Test Over the next several months we’ll work with Filene Research Institute and a cohort of credit unions to: → Educate teams on what agentic AI actually is → Build three real, hands-on POCs in our AgentFlow sandbox → Model time savings, error reduction, and member impact No slideware, just workflows credit unions can touch, test, and evaluate. Excited to help shape the next generation of member service! #CreditUnions# #AgenticAI# #FinTech# #MemberExperience# #Innovation#

# Practical and Useful Patterns for OpenAI Agent SDK 🌍 See what's happening inside your agents. Built-in tracing captures every LLM call, tool execution, handoff, and guardrail evaluation in your agent flow. Tracing is enabled by default. Visualize agent behavior in the Traces dashboard to accelerate debugging and quality improvement. 📌 Title: Tracing 🔗 URL: 🧩 Overview The OpenAI Agent SDK's built-in tracing automatically records every step of agent execution -- LLM calls, tool executions, handoffs, and guardrail evaluations. It's enabled by default and visualized in OpenAI's Traces dashboard. Group related runs with `with trace("name")`, ensure trace delivery in worker environments with `flush_traces()`, and control privacy with `RunConfig(trace_include_sensitive_data=False)` or environment variables. 🛠 How to Use Import `Agent`, `Runner`, and `trace` from `agents`. Create an `Agent` and run it with ` "What are the latest AI trends?")` -- tracing is automatically enabled by default, recording all LLM calls and tool executions. 🏗 Practical Usage Patterns **Grouping Related Runs with trace()** Bundle multiple agent runs into a single trace for a bird's-eye view of the entire workflow. Import `trace` from `agents` and wrap multiple ` calls inside a `with trace("Joke workflow"):` block. The joke generation, evaluation, and conditional improvement flow are grouped into a single trace for end-to-end visibility in the dashboard. **Reliable Trace Delivery with flush_traces()** In Celery/RQ/FastAPI background workers, flush traces before the process exits. Import `flush_traces` from `agents`. In Celery tasks, call `flush_traces()` after ` message))` to send traces. In FastAPI, call `flush_traces()` in the `@app.on_event("shutdown")` hook to guarantee trace delivery before process exit. **Privacy Controls for Sensitive Data** Control tracing in ZDR (Zero Data Retention) environments or when handling personal information. Pass `run_config=RunConfig(trace_include_sensitive_data=False)` to ` (the default is `True`) to omit LLM inputs/outputs and function call data from traces; the default can also be flipped with the `OPENAI_AGENTS_TRACE_INCLUDE_SENSITIVE_DATA` environment variable. To disable tracing entirely, set `OPENAI_AGENTS_DISABLE_TRACING=1` or pass `RunConfig(tracing_disabled=True)`. 💡 Use Cases 📊 Visualize LLM/tool/handoff/guardrail flows in the Traces dashboard with zero-config default tracing 🔗 Group multiple agent runs under with trace("Joke workflow") for end-to-end workflow visibility 🔄 Call flush_traces() in Celery/RQ/FastAPI workers to guarantee trace delivery before process exit 🔒 Protect medical/financial data privacy with trace_include_sensitive_data=False ⚠️ Caveats - Tracing is on by default, so sensitive data may inadvertently appear in traces. Review `trace_include_sensitive_data` settings for production environments. - `flush_traces()` is a blocking operation. Call it in shutdown hooks or task completion, not in the critical request path. - Trace data is sent to OpenAI. For compliance requirements, disable with `OPENAI_AGENTS_DISABLE_TRACING=1` or redirect with custom processors (see next section). ✨ Built-in tracing's greatest strength is "it just works with zero config." Start by checking the dashboard as-is, then add workflow grouping and privacy controls as needed! #OpenAIAgentSDK# #AIAgent#

Welcome Agentforce Coworker. 🤝 Half your morning is tabs. Ask: “What’s important today?” It pulls the 9am, the $950K, and the billing dispute you’d have missed. Then it just acts. Coworker: Ask. Find. Act. Close. See it at Dreamforce. #DF26# #Agentforce#

Welcome Agentforce Operations: Deterministic execution beats probabilistic guesswork. 🛠️ Blueprints now guide specialized agents through complex, multi-step back-office workflows. No more broken handoffs. Full observability, audit trails, and human-in-the-loop control. This is how enterprise AI finally gets real work done. @Salesforce

.@FultonBank shows how Agentforce Coworker helps sales leaders get from data to decisions faster — live at @Dreamforce → Brings pipeline + activity into one view → Flags the opportunities leaders should focus on → Turns that workflow into a reusable Skill From digging through records → acting on what matters