patterngrowinghigh complexity

Agentic AI (ReAct Pattern)

Agentic-ReAct is an agent pattern where an LLM alternates between explicit reasoning steps and concrete actions (tool calls, environment operations) to solve multi-step tasks. The model writes out its thoughts, chooses an action, observes the result, and then iterates this think–act–observe loop until a goal is reached. This enables dynamic planning, adaptive tool use, and context-aware behavior rather than a single-shot response. It is typically implemented via an agent framework that orchestrates tools, memory, and control flow around the LLM.

171implementations
30industries
Parent CategoryAutonomous Systems
01

When to Use

  • When tasks require multiple dependent steps where each step depends on the outcome of previous actions (e.g., diagnose → fetch data → re-evaluate).
  • When the system must coordinate multiple tools or APIs (search, databases, calculators, code runners) in a dynamic, context-driven way.
  • When the problem space is open-ended and cannot be fully captured in a single prompt–response interaction.
  • When you need transparent reasoning traces (Thought/Action/Observation) for debugging, auditing, or human review.
  • When the environment is partially observable or changing, and the agent must adapt its plan based on new observations.
02

When NOT to Use

  • When tasks are simple, single-step Q&A or straightforward text generation where a direct LLM call is sufficient.
  • When strict latency constraints exist (e.g., sub-500ms responses) and multiple LLM/tool calls would violate SLAs.
  • When the action space is extremely high-risk (e.g., direct production database writes, financial transfers) and governance is weak or absent.
  • When tools or APIs are unreliable, slow, or poorly specified, making iterative tool use brittle and frustrating.
  • When you lack observability and monitoring; running autonomous agents without traces and metrics is an anti-pattern.
03

Key Components

  • LLM core (reasoning engine that generates thoughts and action decisions)
  • ReAct prompt template (structured format for thoughts, actions, and observations)
  • Tooling layer (APIs, functions, databases, RAG, calculators, code runners, etc.)
  • Tool router / action dispatcher (executes the chosen tool and returns observations)
  • Control loop / agent runtime (orchestrates think–act–observe iterations)
  • Short-term working memory (scratchpad of thoughts, actions, and observations)
  • Long-term memory (vector store or DB for knowledge and past interactions)
  • Planning module (optional high-level planner for decomposing complex tasks)
  • Guardrails & safety layer (policy checks, constraints, and validation)
  • State management & logging (persistent state, traces, and audit logs)
04

Best Practices

  • Design a clear ReAct schema (e.g., Thought, Action, Action Input, Observation) and enforce it via system prompts or JSON schemas.
  • Constrain the toolset to a small, well-defined set of tools with precise, natural-language descriptions and explicit input/output formats.
  • Use separate roles or modes for planning and execution (e.g., a planner agent that decomposes tasks and an executor agent that runs tools).
  • Implement strict iteration limits, timeouts, and budget caps to prevent infinite loops and runaway tool usage.
  • Log every thought, action, and observation for debugging, evaluation, and compliance auditing.
05

Common Pitfalls

  • Allowing an unbounded number of think–act iterations, leading to loops, high latency, and runaway costs.
  • Exposing too many tools or poorly described tools, causing the agent to choose suboptimal or incorrect actions.
  • Relying solely on the LLM to enforce structure (Thought/Action/Observation) without schema validation or parsing safeguards.
  • Letting the agent perform high-risk actions (e.g., financial transfers, production deployments) without human-in-the-loop approvals.
  • Overloading the context window with full histories instead of summarizing or pruning, which degrades reasoning quality.
06

Learning Resources

07

Example Use Cases

01Autonomous research assistant that plans a research strategy, retrieves documents via RAG, calls web search APIs, and synthesizes a final report with citations.
02Customer support copilot that diagnoses issues by querying knowledge bases, checking ticket history, and proposing step-by-step resolutions.
03Financial analysis agent that pulls market data, runs portfolio simulations via a quant API, and iteratively refines investment scenarios.
04DevOps assistant that inspects logs, queries monitoring systems, proposes remediation steps, and drafts infrastructure-as-code changes for review.
05Sales operations agent that enriches leads from CRM and external APIs, prioritizes them, and drafts personalized outreach sequences.
08

Solutions Using Agentic AI (ReAct Pattern)

27 FOUND
ecommerce14 use cases

Ecommerce Visual Product Search

This AI solution powers image- and multimodal-based product search, letting shoppers find items by snapping a photo, uploading an image, or using rich visual cues instead of text-only queries. By understanding product attributes, style, and context, it delivers more relevant results, boosts product discovery, and increases conversion rates while reducing search friction across ecommerce sites and apps.

entertainment2 use cases

Interactive Game Dialogue

This application area focuses on generating and managing natural-sounding, context-aware spoken dialogue in video games, both for pre-scripted lines and live player interaction. It covers tools and workflows that clean and structure scripts for synthetic voice performance, as well as systems that let players talk to non-player characters (NPCs) in natural language and receive believable, voiced responses in real time. It matters because dialogue is central to immersion, characterization, and gameplay, but traditional pipelines are expensive and rigid: writers must author vast branching scripts, voice actors record thousands of lines, and designers wire everything into dialogue trees and menus. AI-enabled interactive dialogue allows studios to reduce manual authoring and re-recording, improve consistency and quality of performances, and unlock more open-ended, conversational gameplay while keeping production costs and timelines under control.

aerospace defense3 use cases

Autonomous Propulsion Design Optimization

This AI solution uses advanced machine learning and reinforcement learning to co-design and optimize propulsion systems for autonomous aerospace and defense platforms, from unmanned aircraft to multi-phase spacecraft trajectories. By rapidly exploring design spaces, mission profiles, and control strategies in simulation, it accelerates joint development programs, improves fuel efficiency and mission endurance, and reduces the cost and risk of propulsion R&D.

customer service13 use cases

AI Customer Interaction Orchestration

AI Customer Interaction Orchestration centralizes and automates customer-service conversations across chat, messaging, and other digital channels. It uses conversational agents to resolve standard inquiries, guide complex cases, and adapt responses to each customer’s context and history. This improves customer satisfaction while reducing support costs and freeing human agents to focus on high‑value issues.

marketing25 use cases

AI Behavioral Marketing Segmentation

This AI solution uses machine learning to profile customer behavior and dynamically segment audiences across channels. By powering hyper-personalized journeys, targeting, and experimentation, it boosts campaign relevance, increases conversion and lifetime value, and reduces wasted marketing spend.

aerospace defense13 use cases

Aerospace-Defense AI Threat Intelligence

AI systems that fuse multi-domain aerospace and defense data to detect, classify, and forecast physical and cyber threats across air, space, and unmanned platforms. These tools provide real-time situational awareness and decision support for battle management, national airspace security, and autonomous defense systems. The result is faster, more accurate threat assessment that improves mission effectiveness while reducing operational risk and response time.

hr9 use cases

AI-Powered Talent Outreach

AI-Powered Talent Outreach uses machine learning and intelligent agents to source, engage, and nurture candidates across channels, acting as a virtual recruiter and talent CRM. It automates personalized outreach, screening, and follow-ups while maintaining compliance, enabling HR teams and agencies to fill roles faster, reduce manual effort, and improve hiring quality at scale.

sports4 use cases

AI-Powered Sports Fan Engagement

This AI solution uses AI to design and run gamified experiences for sports fans, from interactive apps and fantasy-style challenges to personalized quests and rewards. By powering innovation platforms like LALIGA’s and enabling agentic and conversational AI, it boosts fan engagement, unlocks new revenue streams, and provides clubs and leagues with rich behavioral insights for smarter marketing and product decisions.

hr7 use cases

AI Recruiting & Talent Intelligence

AI Recruiting & Talent Intelligence tools automate candidate sourcing, screening, and engagement while surfacing rich insights about talent pools and hiring funnels. They use machine learning to match candidates to roles, personalize outreach, and analyze multi-channel data to identify best-fit talent. This increases recruiter productivity, shortens time-to-hire, and improves quality and fairness of hiring decisions.

finance50 use cases

Financial Crime & Trading Pattern AI

This AI solution applies advanced pattern recognition and machine learning to detect fraud, money laundering, and anomalous behavior across banking and crypto transactions, while also powering quantitative and algorithmic trading strategies. By continuously learning from transactional, behavioral, and market data, these systems surface hidden financial crime networks, reduce false positives in compliance, and generate trading signals with higher precision. The result is lower fraud losses and compliance risk, alongside more profitable and resilient trading operations.

sports11 use cases

AI Sports Fan Engagement Media

This AI solution uses AI to power interactive sports broadcasts, personalized content discovery, and real-time fan engagement across streaming, social, and in-venue channels. It blends live data, athlete avatars, and automated highlight creation with ad and content optimization to keep fans watching longer and interacting more deeply. The result is higher audience retention, new digital revenue streams, and more effective media monetization for sports leagues and broadcasters.

sports6 use cases

AI Sports Fan Engagement

AI Sports Fan Engagement applications use machine learning, personalization engines, and automation to interact with fans across digital and in-venue channels in real time. They analyze fan behavior and sentiment, generate tailored content (including automated highlights and montages), and provide analytics that help teams and leagues deepen loyalty, grow audiences, and unlock new revenue from sponsorships and ticketing.

marketing8 use cases

AI Marketing Personalization Engine

This AI solution uses AI to personalize marketing interactions across channels, from email to digital campaigns, in real time. By predicting consumer behavior and tailoring content, timing, and offers at the individual level, it increases engagement, conversion rates, and overall marketing ROI while automating execution at scale.

aerospace defense5 use cases

AI-Driven Force Multipliers

This AI solution uses advanced AI, multi-agent systems, and game-augmented reinforcement learning to amplify the effectiveness of aerospace-defense intelligence, planning, and battle management teams. By automating complex analysis, optimizing defensive counter-air operations, and supporting real-time command decisions, it increases mission success rates while reducing required manpower, reaction time, and operational risk.

mining11 use cases

AI-Powered Mining Loading Automation

Suite of AI systems that automate and optimize loading operations across open-pit and underground mines, from shovels and loaders to autonomous haul trucks and cargo drones. These tools use real-time data to improve loading accuracy, reduce cycle times, and cut fuel and energy use while enhancing safety in high‑risk zones. The result is higher throughput, lower operating costs, and more predictable, resilient mining operations.

ecommerce3 use cases

AI Abandoned Cart Conversion

AI Abandoned Cart Conversion uses shopping assistants and agentic checkout flows to re-engage customers who leave items in their carts across web and mobile channels. It personalizes reminders, incentives, and recommendations in real time while automating the outreach and optimization, increasing recovered revenue and improving marketing efficiency for ecommerce brands.

technology19 use cases

AI Coding Quality Assistants

AI Coding Quality Assistants embed large language models into the development lifecycle to generate, review, and refactor code while automatically creating and validating tests. They improve code quality, reduce technical debt, and harden security by catching defects and vulnerabilities early. This increases developer productivity and accelerates delivery of reliable enterprise software with lower maintenance costs.

automotive8 use cases

Automotive AI Systems Integration

This AI solution unifies AI, cloud, and advanced computing into a cohesive systems layer for modern vehicles, spanning ADAS, in-cabin intelligence, wiring harness design, and software-defined architectures. By integrating disparate AI capabilities into a centralized, connected platform, automakers can accelerate feature deployment, reduce engineering complexity, and support scalable autonomous and connected vehicle programs.

aerospace defense15 use cases

AI Geospatial Defense Intelligence

This AI solution applies AI to satellite and geospatial data to automatically detect military assets, maritime threats, gray-zone activity, and environmental risks in near real time. By combining onboard edge processing, multi-sensor fusion, and specialized defense analytics, it turns raw Earth observation data into actionable intelligence for targeting, surveillance, and situational awareness. The result is faster decision-making, improved mission effectiveness, and more efficient use of defense ISR resources.

mining7 use cases

Autonomous Mining Haulage

Autonomous Mining Haulage refers to the use of self-driving trucks, loaders, drills, and aerial vehicles to move ore, waste, and supplies across mine sites with minimal human intervention. These systems use onboard perception, mapping, and planning to navigate complex open-pit and underground environments, coordinate routes, and operate continuously across shifts. The focus is on automating repetitive, heavy mobile equipment tasks such as hauling, loading, and short-range logistics that are traditionally labor-intensive and exposed to high safety risks. This application matters because haulage and material movement are among the largest cost and bottleneck drivers in mining operations, and they are also a major source of accidents and downtime. By automating haul trucks, underground loaders, and cargo drones, mining companies can reduce dependence on scarce skilled operators, improve safety by removing people from hazardous zones, and achieve more consistent, predictable production. The result is lower cost per ton, higher equipment utilization, and more stable throughput from pit or stope to processing plant.

finance17 use cases

AI Financial Crime & SAR Intelligence

This AI solution uses AI to detect, investigate, and report suspicious activity across banks, wealth managers, and other regulated financial institutions. It combines transaction monitoring, crypto tracing, fraud detection, and regulatory analysis to streamline AML reviews and generate higher-quality Suspicious Activity Reports. The result is faster detection of financial crime, reduced compliance cost, and lower regulatory and reputational risk.

finance10 use cases

AI Transaction Compliance Monitoring

This AI solution uses AI to automatically monitor financial transactions, detect suspicious patterns, and streamline AML/KYC reviews across banks, wealth managers, and other financial institutions. It replaces manual investigations with intelligent agents and APIs that continuously flag, prioritize, and explain risk events, improving regulatory compliance while cutting review times and false positives. The result is stronger AML controls, lower compliance costs, and reduced risk of regulatory penalties and financial crime exposure.

advertising9 use cases

AI Ad Trend Intelligence

AI Ad Trend Intelligence analyzes historical and real-time advertising data to forecast market shifts, audience behavior, and creative performance across channels. It guides marketers on where to spend, which messages and formats to use, and how to optimize campaigns for maximum ROI. By turning complex trend signals into actionable recommendations, it boosts revenue impact while reducing wasted ad spend.

construction6 use cases

AI Construction Cost & Asset Forecasting

This AI solution uses AI to forecast labor needs, equipment performance, material usage, and lifecycle costs across construction projects and fleets. By combining predictive workforce planning, digital-twin–driven cost simulations, and maintenance optimization, it helps contractors reduce overruns, extend asset life, and improve bid accuracy and project profitability.

insurance15 use cases

AI Insurance Claims Orchestration

This AI solution uses AI to triage, validate, and process insurance claims end-to-end across property, casualty, and medical lines. By automating document intake, fraud checks, coverage validation, and payment decisions, it accelerates claim resolution, reduces manual effort, and improves payout accuracy and customer experience.

advertising5 use cases

AI Ad Creative Ideation Suite

This AI solution uses generative AI to rapidly explore, iterate, and refine advertising concepts across formats like video, image, and copy. It transforms loose ideas into testable creative assets at scale, helping brands and agencies accelerate campaign development, boost creative performance, and reduce production costs.

advertising7 use cases

AI Programmatic Ad Optimization

AI Programmatic Ad Optimization uses machine learning agents to generate ad creative, test copy variations, and autonomously manage programmatic buying across channels. It analyzes performance in real time to fine-tune targeting, bids, and creatives, maximizing ROAS and lowering customer acquisition costs while reducing manual campaign management effort.