15 AI Agent Examples Running in Production Today

The first AI software engineer marketed as fully autonomous. Devin takes natural language task descriptions (“add OAuth to our Express app”), decomposes them into steps, writes code, runs tests, debugs failures, and iterates. Nubank reported 12x efficiency improvements and 20x cost savings when using Devin for multi-million-line codebase migrations.

Architecture: LLM agent loop in sandboxed cloud environment with shell, editor, and browser. Infrastructure: Dedicated compute per session, persistent file system, network access for package installation. Price dropped from $500/month to $20/month in late 2025.

An open-source framework that turns any LLM into a software engineering agent. SWE-Agent resolves real GitHub issues by reading code, making edits, and running tests. It pioneered the Agent-Computer Interface (ACI) design pattern — a structured way for LLMs to interact with development environments.

Architecture: LLM + ACI with Docker-based environment isolation. Infrastructure: Docker host with GPU access for local models, or API keys for cloud LLMs. Each agent run spawns a container. Self-hostable on any Linux server.

Launched November 2025 with the highest verified SWE-bench scores at the time (76.2%). Antigravity coordinates up to 8 specialized sub-agents simultaneously for complex multi-component projects. Its Planning mode decomposes large tasks before any code is written.

Architecture: Multi-agent orchestration with specialized planners, coders, and reviewers. Infrastructure: Cloud-hosted by Google. Each session runs across multiple parallel compute units. Not self-hostable.

Zendesk's native AI agents resolve tickets without human intervention using the company's knowledge base and historical ticket data. They handle intent detection, answer generation, and escalation routing. Enterprise deployments report 60-80% autonomous resolution rates on tier-1 tickets.

Architecture: RAG pipeline over knowledge base + intent classification + response generation. Infrastructure: Hosted by Zendesk. Custom deployments integrating external LLMs need persistent servers for the RAG pipeline and vector database.

Fin is Intercom's AI agent that answers customer questions using the company's existing help center content. It handles multi-turn conversations, provides cited sources for each answer, and seamlessly hands off to human agents when it cannot resolve an issue. Intercom reports that Fin resolves up to 50% of support volume immediately.

Architecture: LLM-based retrieval with source citation and confidence scoring. Infrastructure: Hosted SaaS. Teams building custom support agents with similar capabilities need a VPS for the LLM orchestration layer and a vector database for knowledge retrieval.

Many companies build custom support agents using LangChain or LlamaIndex for RAG over their specific documentation, integrated with Slack, Discord, or custom chat interfaces. These offer more control over the retrieval pipeline, response formatting, and escalation logic than SaaS solutions.

Architecture: Custom RAG pipeline + LLM + webhook integrations. Infrastructure: Dedicated server with 8-16 GB RAM for the vector DB and orchestration layer. Persistent process for webhook listeners. Needs 24/7 uptime — a down support bot means unanswered customers.

Perplexity's agent system queries the web, synthesizes information from multiple sources, and generates cited research reports. Their latest “Computer” agent (launched February 2026) integrates 19 AI models into a single environment that can generate sub-agents for specialized tasks like financial analysis, data collection, and visualization generation.

Architecture: Multi-model orchestration with web scraping, citation extraction, and report generation. Infrastructure: Cloud-hosted with GPU clusters for parallel model inference. The $200/month Max tier reflects the compute intensity.

Companies deploy agents that continuously monitor competitor pricing, product launches, job postings, and press releases. These agents scrape public data sources on a schedule, compare against historical baselines, and generate alerts when significant changes are detected. Unlike one-off research, these run 24/7.

Architecture: Scheduled scraping + LLM summarization + anomaly detection + notification pipeline. Infrastructure: Persistent server with cron-based scheduling, database for historical data, and enough RAM for the LLM inference or API budget for cloud LLM calls.

Modern trading agents go beyond simple algorithmic strategies. They ingest tick data, news feeds, and alternative data signals, use RL-trained policies for execution optimization, and operate within strict latency and compliance boundaries. JPMorgan's LOXM system uses deep RL trained on billions of historical trades to optimize large equity order execution.

Architecture: RL policy network + market data ingestion + execution engine + risk management layer. Infrastructure: Ultra-low-latency dedicated servers, co-located with exchanges. GPU access for policy inference. Persistent state for portfolio tracking. Failure recovery measured in milliseconds.

LLM-based agents that analyze earnings reports, SEC filings, and financial news to generate investment summaries and risk assessments. They parse structured financial data (balance sheets, income statements) and unstructured text (management commentary, analyst calls) to produce comprehensive analysis that would take a human analyst hours.

Architecture: Document parsing + structured data extraction + LLM reasoning + report generation. Infrastructure: Server with 16-32 GB RAM for document processing, persistent storage for financial data, and scheduled execution for earnings season batch processing.

Agents that monitor infrastructure alerts, correlate symptoms across services, diagnose root causes, and execute runbooks automatically. Companies like Ciroos combine AI agents with SRE workflows using multi-agent architectures to automate incident management and reduce operational toil. When PagerDuty fires at 3 AM, the agent handles the first response.

Architecture: Alert ingestion + log correlation + runbook execution engine + escalation logic. Infrastructure: Must run independently of the infrastructure it monitors. Dedicated server with access to monitoring APIs, SSH keys for remediation, and its own monitoring (who watches the watcher).

Agents that review pull requests, identify potential bugs and security vulnerabilities, suggest fixes, and auto-merge when confidence is high. They integrate with GitHub Actions or GitLab CI and use LLMs to understand code context beyond what static analysis catches. Some teams deploy agents that automatically fix failing tests by analyzing the error output and generating patches.

Architecture: Webhook listener + code analysis + LLM review + PR integration. Infrastructure: Persistent server for webhook endpoints, sufficient RAM for code context loading (large repos need 8-16 GB), API access to LLM providers.

Agents that perform initial patient symptom assessment, suggest possible conditions, and route to appropriate specialists. They maintain contextual awareness across patient records and support clinical decision-making. The healthcare AI agent market could save the US healthcare system up to $150 billion annually by reducing administrative burden.

Architecture: Symptom classifier + medical knowledge RAG + risk assessment + routing. Infrastructure: HIPAA-compliant hosting with encrypted storage, audit logging, and access controls. No shared infrastructure. Dedicated servers with tamper-proof logs are mandatory.

Agents that review contracts, flag non-standard clauses, compare terms against company policies, and generate risk summaries. A task that takes a junior associate 4 hours can be completed in minutes. These agents need extremely high accuracy — a missed liability clause has real financial consequences.

Architecture: Document parsing + clause extraction + policy comparison + risk scoring. Infrastructure: On-premises or dedicated servers for data sovereignty. 32 GB+ RAM for loading large contract corpora. Audit logging for regulatory compliance.

Agents that analyze data from thousands of IoT sensors to optimize machine settings and detect early signs of equipment failure. They predict maintenance needs before breakdowns occur, reducing unplanned downtime by 30-50% in deployed facilities. Retailers apply similar agents for inventory optimization across warehouse networks.

Architecture: Sensor data ingestion + anomaly detection + predictive models + alerting. Infrastructure: Edge servers near production lines for low-latency sensor data processing, with cloud aggregation for fleet-wide analysis. 24/7 operation is mandatory — a missed sensor anomaly means equipment damage.