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Choose the right model, simplify your build

Browse models by performance, cost, and use case so you can make the right decision, faster. Start with your goal to narrow down the best options.
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claude-fable-5-1
claude-fable-5-1messages
Input typetext, image, code
Output typetext, image, code
Context window1000k
Token limits128k output
Preview
gpt-6-astra
gpt-6-astrachat-completion
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Output typetext
Context window1050k
Token limits128k output
Generally available (GA)
Microsoft AI
MAI-Image-2.6text-to-image
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Output typeimage
Context window32k
Token limits4.096k output
Preview
DeepSeek-V4-Flash
DeepSeek-V4-Flashchat-completion
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Output typetext
Context window1000k
Token limits128k output
Generally available (GA)
FW-Kimi-K3
FW-Kimi-K3chat-completion
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Output typetext
Context window1048.576k
Generally available (GA)
Qwen
qwen--qwen3.8-27bchat-completion
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Context window262.144k
Generally available (GA)
MiniMax
FW-MiniMax-M3chat-completion
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Context window1048.576k
Generally available (GA)
model-router
model-routerchat-completion
Input typetext, image
Output typetext
Context window1048.576k
Token limits32.768k output
Generally available (GA)

Pick an agent, ship a solution

Browse configurable agents organized by common business scenarios to turn ideas into working systems, all without starting from scratch.
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Industry News & Trend Scanner
What this agent manifest does Autonomous industry briefing — scans, filters, ranks, and delivers a cited news summary. Build an agent that takes an industry or sector and delivers a concise, cited briefing of the most relevant recent news and trends. The agent runs several Bing-grounded searches — industry news, trend analysis, specific company or topic queries — deduplicates results, ranks by relevance and authority, and formats the output as a scannable briefing with top stories, a trend-watch section, and quick hits. Facts are Bing-grounded with source citations, reflecting public information available at the time of the search. This is a single-tool autonomous scan with editorial filtering pattern using numbered-rule prompt structure. With only one tool, the prompt carries all the complexity: the search strategy (at least four distinct queries), deduplication logic, relevance ranking, and the line between editorial coverage and promotional content. Numbered rules make the agent's filtering decisions transparent and repeatable. The prompt handles sparse news periods (report honestly rather than pad) and developing stories with conflicting details (note the uncertainty rather than pick a side). Because it runs autonomously, the briefing is a draft for a person to read and act on. This pattern applies anywhere an agent needs to scan a source, apply editorial judgment to filter and rank, and deliver a curated summary. Common scenarios for this pattern: Technology trend monitoring for R&D teams Competitor product-launch tracking Open-source ecosystem change monitoring Market-signal detection for strategy teams Conference and event coverage summaries The tools it uses This manifest uses: Bing Web Search Use this agent manifest to understand How to build autonomous agents using numbered rules prompt structure for briefing / report output.
Competitive Landscape Researcher
What this agent manifest does Autonomous multi-source research, structured into a formatted report — no human checkpoints. Build an agent that takes a list of competitors or a market segment and produces a structured landscape report in one pass. It runs Web Search queries per competitor to gather recent developments, public financials, product launches, and positioning, then hands the raw findings to code interpreter to build comparison tables, identify patterns across the set, and compile a cited report. Findings reflect publicly available information at the time of the search. This is an autonomous gather → process → deliver pattern using markdown-header prompt structure. Each phase has explicit completion criteria so the agent moves through research, structuring, and compilation without waiting for input. The scope boundaries hold it to facts — no editorializing about who's "winning," no speculation about unannounced plans — and the edge-case handling covers private companies with thin data, conflicting sources, and overly broad requests. As an autonomous agent, the report is a draft for the analyst to review. This pattern applies anywhere an agent pulls from multiple sources, synthesizes, and produces a structured deliverable in one pass. Common scenarios for this pattern: Due-diligence research Vendor or technology landscape assessments Market-entry analysis Internal competitive briefings for sales Industry benchmarking reports The tools it uses This manifest uses: Bing Web Search, code interpreter Use this agent manifest to understand How to build autonomous agents using markdown headers prompt structure for report / tables output.
Internal Policy Q&A
What this agent manifest does Plain-language answers to policy questions, sourced from SharePoint — one question, one answer, one reference. Build an agent that answers employee policy questions by searching SharePoint for the relevant document and responding in conversational language with a citation to the source. The agent handles the full range of lookup scenarios: clear answers get a 2-4 sentence summary, context-dependent answers include conditions and exceptions, ambiguous questions get the most likely interpretation with a follow-up offer, and out-of-scope questions get a polite redirect to the right person. This is a single-tool Q&A with exhaustive edge-case handling pattern using natural-language prompt structure. With one tool and a single-shot interaction, the prompt carries all the weight — there's no multi-step orchestration to fall back on. The instructions define exactly how to handle five distinct response scenarios, drawing precise boundaries around what the agent should and shouldn't do. The defining discipline is grounding: every answer comes from a found document and cites it, and "I couldn't find that" is a first-class answer rather than a guess. It never gives legal interpretation. This pattern applies anywhere an agent answers questions from a document library with no room for misinterpretation or hallucination. Common scenarios for this pattern: IT knowledge-base self-service Benefits and perks FAQ Vendor-contract terms lookup Product-documentation Q&A for customers Internal process and procedure reference The tools it uses This manifest uses: SharePoint Use this agent manifest to understand How to build single-shot agents using natural language prompt structure for narrative answer + reference output.
Trip Itinerary Designer
What this agent manifest does Collaborative trip planning — interviews you about travelers and preferences, builds a sequenced day-by-day itinerary. Build an agent that works like a travel-planning partner: it asks about your destination, travelers, budget, interests, and constraints, then uses Web Search for current options to build a sequenced day-by-day itinerary with activities, logistics, accommodation ideas, and cost estimates. It branches by context — solo business trips get efficiency-focused plans, family trips get varied pacing, short trips get ruthless prioritization, luxury trips get curated experiences. Options reflect publicly available information at the time of the search. This is a collaborative planning with multi-axis branching pattern using natural-language prompt structure. Rather than a simple if/else tree, the agent branches on several dimensions at once — traveler type, duration, budget tier, interest profile — all shaping the output. Memory retains the planner's preferences and past itinerary patterns across sessions. The prompt shows how natural-language instructions can encode complex branching that feels like a conversation. The guardrails are firm: it never books anything and treats all prices as approximate. This pattern applies anywhere an agent co-creates a structured plan through conversation, adapting to multiple user-provided variables. Common scenarios for this pattern: Corporate offsite agenda planning Conference or summit scheduling Relocation city-exploration guides Client-visit itineraries for account teams Team-retreat activity coordination The tools it uses This manifest uses: Bing Web Search, File search Use this agent manifest to understand How to build multi-turn agents using natural language prompt structure for day-by-day itinerary output.
Sales Metrics Dashboard Builder
What this agent manifest does Interactive dashboard building — describe the metrics you need, get formatted charts and tables from Fabric data. Build an agent that queries your Fabric workspace for sales data via a data agent, runs calculations in code interpreter, and delivers formatted charts, tables, and summary statistics. It works interactively: it clarifies metric definitions (is "revenue" bookings, ARR, or recognized?), confirms dimensions and time ranges, then runs a four-step pipeline — discovery, extraction, computation, formatting. Upload a CSV alongside Fabric data to enrich the analysis. This is an interactive requirements → multi-step data pipeline pattern using markdown-header prompt structure. The agent clarifies before building — a focused batch of questions about metrics, dimensions, granularity, and filters — which avoids the classic data-agent failure of computing the wrong metric confidently. Separating data discovery (what's available) from extraction (what's needed) makes it reliable on unfamiliar datasets. Calculations and any charts run in code, so the output is a draft to review. This pattern applies anywhere an agent clarifies analytical requirements through conversation, then runs a multi-step data pipeline. Common scenarios for this pattern: Marketing-funnel analysis from warehouse data Operational KPI dashboards from live sources Customer-cohort analysis for product teams Inventory-turnover and demand reporting Revenue-recognition and billing reconciliation The tools it uses This manifest uses: data agent, code interpreter Use this agent manifest to understand How to build multi-turn agents using markdown headers prompt structure for dashboard / tables output.
Brand Mention Monitor
What this agent manifest does Autonomous brand monitoring — scans, categorizes by sentiment and reach, escalates negative mentions. Build an agent that scans the web for mentions of your brand, products, or executives via Bing-grounded search, deduplicates results in code interpreter, classifies each mention by sentiment (positive, neutral, negative, crisis) and reach (high, medium, low), and delivers a prioritized summary. It follows different output paths based on what it finds: all-positive gets a standard summary, negative mentions get flagged with context, and crisis-level mentions trigger an urgent header with escalation considerations. It produces the report; the user decides what to do. Findings reflect public information available at the time of the search. This is an autonomous scan with conditional escalation pattern using numbered-rule prompt structure. The agent makes judgment calls without human input — classifying sentiment and reach and deciding whether something meets crisis criteria (3+ major outlets, legal allegations, viral discussion). Numbered rules create an unambiguous tree: scan → deduplicate → classify → route to the matching output path. The prompt handles brand-name ambiguity (exclude when uncertain), conflicting sources, and the line between editorial coverage and promotion. As an autonomous monitor, its output is a draft for the team to act on. This pattern applies anywhere an agent monitors a source, classifies by severity, and routes output based on what it discovers. Common scenarios for this pattern: Product-review monitoring across platforms Social-sentiment tracking for campaigns Competitor-announcement detection Press-coverage tracking for PR teams Community-forum issue escalation The tools it uses This manifest uses: Bing Web Search, code interpreter Use this agent manifest to understand How to build autonomous agents using numbered rules prompt structure for summary / report output.
Blog Post Drafter
What this agent manifest does Multi-stage content creation with gated approvals and persistent style memory. Build an agent that turns a topic brief into a publish-ready blog post through a three-stage conversation: research and outline, full draft, then iterative refinement. The agent uses Web Search to pull current information on the topic and File search to read any uploaded style guides or sample posts — matching voice and structure to the user's brand. With memory enabled, it retains voice preferences and structural patterns across sessions. Researched facts reflect public information available at the time of the search. This is a staged creation with human gates pattern using natural-language prompt structure. Each stage produces an intermediate output the user approves before the agent proceeds, which keeps subjective work aligned without over-constraining the writing. Memory adds a compounding layer — a persistent model of the user's preferences, so session 10 is meaningfully better than session 1. The instructions define just enough process to be reliable while leaving room to adapt tone, structure, and angle per piece. The originality rule is firm: research informs the post, but the writing is original. This pattern applies anywhere an agent produces subjective or creative deliverables that need human judgment at key decision points. Common scenarios for this pattern: Proposal and RFP draft workflows Executive communications and talking points Marketing copy and campaign messaging Product documentation and changelogs Internal announcements where tone matters The tools it uses This manifest uses: Bing Web Search, File search Use this agent manifest to understand How to build multi-turn agents using natural language prompt structure for narrative draft output.
Codebase Documentation Generator
What this agent manifest does Autonomous repo documentation — READMEs, module docs, and architecture summaries from real code analysis. Build an agent that analyzes a GitHub repository and generates review-ready documentation: a project-level README, module-level docs for each major directory, and an architecture summary mapping component relationships. The agent reads repo structure and source via GitHub, uses code interpreter to parse imports, function signatures, and class hierarchies, builds a dependency map, and generates documentation from what it actually finds in the code — not from assumptions about what the code should do. This is an autonomous analysis → generation → self-review pattern using markdown-header prompt structure. What makes it distinctive is the self-review step: before delivering, the agent validates its own output — every documented function exists in the repo, module names match real directories, no placeholder text remains. The prompt draws a hard line between describing what code does (acceptable) and assuming why it does it (not). Because it runs autonomously and produces code-adjacent artifacts, the output is a draft to review before committing. Edge cases cover empty repos, multi-language codebases, and conflicts between existing docs and actual code. This pattern applies anywhere an agent analyzes a complex system, generates structured documentation, and validates its own output before delivery. Common scenarios for this pattern: API reference generation from source Infrastructure documentation from configuration files Data-pipeline documentation from DAG definitions Onboarding guides generated from repo structure Architecture notes from code patterns The tools it uses This manifest uses: GitHub, code interpreter Use this agent manifest to understand How to build autonomous agents using markdown headers prompt structure for documentation files output.

Connect your tools, optimize your data

Use APIs and knowledge sources to support real-world workflows. Give your agents access to the information they need to act.
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Microsoft DataverseEnables Dataverse data and schema operations (query, table & record management, API invocation). This feature is in preview and is part of the Work IQ tools, providing shared work intelligence and actions for agents. Availability and capabilities are subject to change. Learn more: https://aka.ms/AboutWorkIQ. You need to publish your Dataverse MCP server with Agent365 CLI and get admin approval in Microsoft365 Admin Center.
Remote MCP
Work IQ TeamsWork IQ Teams MCP Server which enables core Microsoft Teams actions, including chat, channel, and team management, and message creation/retrieval. This feature is in preview and is part of the Work IQ tools, providing shared work intelligence and actions for agents. Availability and capabilities are subject to change. Learn more: https://aka.ms/AboutWorkIQ
Remote MCP
Work IQ WordWork IQ Word MCP Server which enables Microsoft Word document creation, retrieval, and commenting actions for agentic workflows. This feature is in preview and is part of the Work IQ tools, providing shared work intelligence and actions for agents. Availability and capabilities are subject to change. Learn more: https://aka.ms/AboutWorkIQ
Remote MCP
Work IQ CopilotWork IQ Copilot MCP Server which enables core Microsoft Copilot chat actions, including starting conversations and sending messages with contextual grounding. This feature is in preview and is part of the Work IQ tools, providing shared work intelligence and actions for agents. Availability and capabilities are subject to change. Learn more: https://aka.ms/AboutWorkIQ
Remote MCP
Work IQ CalendarWork IQ Calendar MCP Server which enables core Outlook calendar actions (create, update, manage, and schedule events) for agentic workflows. This feature is in preview and is part of the Work IQ tools, providing shared work intelligence and actions for agents. Availability and capabilities are subject to change. Learn more: https://aka.ms/AboutWorkIQ
Remote MCP
Work IQ MailWork IQ Mail MCP Server which enables Microsoft Outlook email actions (create, send, reply, search) for agentic workflows. This feature is in preview and is part of the Work IQ tools, providing shared work intelligence and actions for agents. Availability and capabilities are subject to change. Learn more: https://aka.ms/AboutWorkIQ
Remote MCP
Azure Databricks GenieAzure Databricks Genie MCP server lets AI agents connect to Genie spaces so users can ask natural language questions and get specialized answers from their data easily.
Remote MCP
Work IQ UserWork IQ User MCP Server which enables Microsoft 365 user profile and directory lookups (user details, managers, direct reports, and org listings). This feature is in preview and is part of the Work IQ tools, providing shared work intelligence and actions for agents. Availability and capabilities are subject to change. Learn more: https://aka.ms/AboutWorkIQ
Remote MCP

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