At Smartsheet, we chose Strands for our next generation of AI capabilities because it provided the perfect balance of enterprise-ready features and development efficiency. Its robust conversation memory and dynamic tool registration systems were crucial for creating a responsive, context-aware intelligent AI assistant. With Strands, we were able to quickly implement a secure and scalable solution, giving us a production-ready foundation to deliver a secure, high-performance, and enterprise-grade AI experience.
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Use Cases
Built for the agent you need
Automate multi-step processes, ship assistants that reason over your own data, run research agents that work autonomously, or control robots in the physical world.
strands-agents/samples1
import { createHarness } from '@strands-agents/harness'
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import { tool } from '@strands-agents/sdk'
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import z from 'zod'
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const classifyLead = tool({
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name: 'classify_lead',
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description: 'Score and classify a lead.',
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inputSchema: z.object({
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email: z.string(),
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company: z.string(),
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}),
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callback: ({ email, company }) => {
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const data = crm.lookup(company)
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return {
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leadId: crm.createLead(email, company),
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score: computeIcpScore(data),
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segment: data.industry,
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}
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},
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})
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const routeToRep = tool({
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name: 'route_to_rep',
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description: 'Assign a lead to a rep.',
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inputSchema: z.object({
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leadId: z.string(),
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region: z.string(),
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}),
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callback: ({ leadId, region }) => {
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const rep = crm.getRepForRegion(region)
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crm.assign(leadId, rep)
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return `Assigned to ${rep}`
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},
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})
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const agent = await createHarness({
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tools: [classifyLead, routeToRep],
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})
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await agent.invoke(
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'New lead: jane@acme.com, Acme Corp, US-West'
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)
Transform traditional error alerts into intelligent incident responses using Amazon Bedrock, RAG with Amazon OpenSearch, Multi-Agent Orchestration with Strands SDK, and Kiro AI IDE - reducing MTTR by 60% without manual coding.
Strands’ SDK and great integration with AWS native services streamlined Landchecker’s development of agents. With easier integration of AgentCore Runtime, Bedrock Guardrails, and built-in support for OpenTelemetry, we could focus on what we do best – developing property information tools and data integrations.
At Swisscom, we need an agentic AI backbone that is both enterprise-ready and future-proof. Strands Agents gives us the best of both worlds: a native fit with our cloud environment, yet fully open source and flexible. That combination allowed us to build proof-of-concepts within just a few weeks and now sets us on the path to scale multi-agent systems with confidence, while keeping our focus on delivering real value to customers and the business.
The advisor is where things get interesting. We use the Strands Agents SDK to define an agent with a tool, a function the model can call during its reasoning loop.
We chose Strands because it’s AWS-native, intuitive, and made agent development accessible across our engineering team. Its abstraction layer and built-in multi-agent patterns (like Agent-as-Tool and Swarm) let us focus on remediation logic instead of infrastructure work. We’ve already built multiple agents, and wiring them together has been seamless. On top of that, we layered our Agentic Remediation™ capability to automate vulnerability fixes and configuration validation/fault correction workflows, coordinating cross-agent remediation with precision
Scaling our global trading platform required reimagining our support capabilities, and Strands Agents was the key to making it happen at enterprise scale. What would traditionally take months of development, Strands allowed us to achieve in just 10 days - delivering a secure, robust, production-ready agentic solution. The results speak for themselves: investigation time dropped on average from 30 minutes to 45 seconds, investigation quality improved by 94%, and we saved $5M in operational costs. Strands didn’t just accelerate our development - it gave us the confidence to explore other agentic AI use cases across our entire business, including launching our Agentic Security Operations Center
Adding bidirectional voice to my existing Strands agent was surprisingly straightforward. BidiAgent handles the WebSocket complexity and interruption logic, my @tool functions carried over unchanged, and the same code deploys to AgentCore without modification. Strands made real-time voice feel like a natural extension, not a separate project.
We see Strands as a great fit to power TeamForm’s next evolution of Agentic AI. Our customers need enterprise-grade security and scalability, which is exactly what Strands delivers. Its seamless integration with AWS and simplicity enables us to focus on innovating our AI capabilities and delivering value to our customers.
For Jit’s infrastructure drift detection agent, we leverage Strands Agents, an open-source framework developed by AWS for building production-ready AI agents. Strands Agents provides several advantages including simplified development, native AWS integration, and built-in security.
As someone who builds agents with LangGraph daily at work, Strands was a genuine surprise. The model-driven approach cut my setup from 40 lines to 3 — and for the 80% case, it just works without sacrificing flexibility.
Strands Agents on Bedrock turns autonomous agents into an enterprise product: governed, observable, and safe by design. Together with Claude models, we analyze live webpages and generate code responsibly - helping customers reduce risk while accelerating delivery. Safety is non-negotiable in offensive security. On Amazon Bedrock, Strands Agents plus Claude let us scale autonomous pen-testing with Bedrock Guardrails - increasing coverage without increasing risk.
The combination of the Strands Agents SDK and Tavily represents a significant advancement in enterprise-grade research agent development. This integration can help organizations build sophisticated, secure, and scalable AI agents while maintaining the highest standards of security and performance. Learn more in this blog.
Strands was used to build a growing set of agents that run a company to do actual tasks.