{"id":7492,"date":"2026-06-09T05:02:37","date_gmt":"2026-06-09T05:02:37","guid":{"rendered":"https:\/\/www.coffee.ai\/articles\/best-crm-task-automation-2026\/"},"modified":"2026-06-09T05:02:37","modified_gmt":"2026-06-09T05:02:37","slug":"best-crm-task-automation-2026","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/best-crm-task-automation-2026","title":{"rendered":"Best CRM with AI Task Automation for Sales Teams 2026"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>True AI task automation in 2026 means systems that autonomously capture data, log activities, and update records after initial setup.<\/li>\n<li>Seven evaluation criteria distinguish genuine automation from rule-based assistants, including automation depth, data quality, and quantified time savings.<\/li>\n<li>Among leading platforms, only Coffee and Salesforce Agentforce deliver fully autonomous task execution; Coffee uniquely offers both standalone and companion-layer deployment.<\/li>\n<li>Coffee saves sales teams 8\u201312 hours per week by eliminating manual data entry and activity logging across email, calendar, and call transcripts.<\/li>\n<li>Teams ready to eliminate passive CRM data chores can <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">get started with Coffee<\/a> today.<\/li>\n<\/ul>\n<h2>Evaluation Criteria for AI Task Automation Depth<\/h2>\n<p>Seven criteria separate genuine automation from marketing copy. The first two, automation depth and data quality, define the underlying architecture. The next three, time savings, implementation effort, and deployment fit, measure day-to-day value. The final two, stack consolidation and pipeline intelligence, show whether the platform simplifies or complicates operations.<\/p>\n<p><strong>1. Automation Depth.<\/strong> Does the platform execute multi-step workflows without human initiation, or does it only surface recommendations? <a href=\"https:\/\/teradata.com\/insights\/ai-and-machine-learning\/what-is-autonomous-ai\" target=\"_blank\" rel=\"noindex nofollow\">Autonomous AI is goal-based and dynamic, selecting among options and handling edge cases<\/a>, while rule-based assistants follow fixed decision trees.<\/p>\n<p><strong>2. Data Quality.<\/strong> Does the system ingest unstructured data, such as email threads, call transcripts, and meeting notes, and convert it into structured CRM fields? Legacy relational databases discard historical context when fields are overwritten.<\/p>\n<p><strong>3. Quantified Time Savings.<\/strong> <a href=\"https:\/\/www.computerworld.com\/article\/4081838\/genai-can-save-employees-7-5-hours-a-week-survey.html\" target=\"_blank\" rel=\"noindex nofollow\">Surveys indicate that AI users save an average of 2.5 to 7.5 hours per week<\/a> on manual tasks such as research and data entry. Platforms that deliver only copilot suggestions recover far less time. Activity capture tools can reduce CRM administration time and reclaim several hours per week. Coffee\u2019s autonomous agent targets the 8\u201312 hour threshold mentioned earlier.<\/p>\n<p><strong>4. Implementation Effort.<\/strong> Teams should measure setup in days or weeks. A platform that requires months of admin configuration before delivering value does not function as a productivity tool.<\/p>\n<p><strong>5. Standalone vs. Companion Fit.<\/strong> The strongest platforms can serve as the system of record for early-stage teams and also operate as an intelligence layer on top of Salesforce or HubSpot for established teams.<\/p>\n<p><strong>6. Stack Consolidation.<\/strong> Effective agents replace point solutions such as enrichment tools, conversation intelligence, and forecasting add-ons instead of adding another subscription to an already fragmented stack.<\/p>\n<p><strong>7. Pipeline Intelligence.<\/strong> <a href=\"https:\/\/creatio.com\/glossary\/ai-crm\" target=\"_blank\" rel=\"noindex nofollow\">McKinsey estimates AI implementation in CRM can increase leads by more than 50% and reduce costs by up to 60%<\/a>. Pipeline analytics only reach that potential when upstream data capture is complete and accurate.<\/p>\n<h2>Side-by-Side Comparison: Automation Depth and Data Quality Across Leading Platforms<\/h2>\n<p>The table below applies these seven criteria to leading platforms and highlights where each one delivers autonomous execution versus copilot-style suggestions. Focus on the \u201cAutonomous Task Execution\u201d column, because it reveals which tools actually act on your behalf.<\/p>\n<table>\n<thead>\n<tr>\n<th>Platform<\/th>\n<th>Autonomous Task Execution<\/th>\n<th>Companion-Layer Option<\/th>\n<th>Reported Weekly Time Savings<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Salesforce Agentforce<\/td>\n<td><a href=\"https:\/\/pandadoc.com\/blog\/top-ai-crm-systems\" target=\"_blank\" rel=\"noindex nofollow\">Yes, Atlas Reasoning Engine executes multi-step workflows independently<\/a><\/td>\n<td>Native (Sales Cloud)<\/td>\n<td><a href=\"https:\/\/www.computerworld.com\/article\/4081838\/genai-can-save-employees-7-5-hours-a-week-survey.html\" target=\"_blank\" rel=\"noindex nofollow\">Broad AI-user average (see criteria above)<\/a>, enterprise configuration required<\/td>\n<\/tr>\n<tr>\n<td>HubSpot Breeze<\/td>\n<td><a href=\"https:\/\/aimultiple.com\/agentic-crm\" target=\"_blank\" rel=\"noindex nofollow\">Partial, Breeze Agents automate end-to-end workflows, most actions still require human initiation<\/a><\/td>\n<td>Native (Sales Hub)<\/td>\n<td>Not independently published, copilot-tier savings estimated at 2\u20133.5 hrs per week<\/td>\n<\/tr>\n<tr>\n<td>monday CRM<\/td>\n<td>Partial, Lexi agent handles lead sourcing and outreach, core CRM logging remains rule-based<\/td>\n<td>No standalone companion mode<\/td>\n<td>Sequence automation saves about 1 hr per day per rep on follow-up tracking<\/td>\n<\/tr>\n<tr>\n<td>Pipedrive<\/td>\n<td><a href=\"https:\/\/pandadoc.com\/blog\/top-ai-crm-systems\" target=\"_blank\" rel=\"noindex nofollow\">No, AI Sales Assistant is limited to recommendations and next-best actions, no autonomous execution<\/a><\/td>\n<td>No<\/td>\n<td>Not published<\/td>\n<\/tr>\n<tr>\n<td>Creatio<\/td>\n<td><a href=\"https:\/\/creatio.com\/glossary\/ai-crm\" target=\"_blank\" rel=\"noindex nofollow\">Yes, agents embedded at architecture level trigger actions and move processes forward without human input<\/a><\/td>\n<td>No dedicated companion mode<\/td>\n<td>Not independently published<\/td>\n<\/tr>\n<tr>\n<td>Coffee<\/td>\n<td>Yes, agent autonomously creates contacts, logs activities, enriches records, and writes pipeline changes from emails, calendars, and transcripts<\/td>\n<td>Yes, deploys as companion layer on Salesforce or HubSpot<\/td>\n<td>8\u201312 hrs per week per rep<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Salesforce Agentforce<\/strong> is <a href=\"https:\/\/aimultiple.com\/agentic-crm\" target=\"_blank\" rel=\"noindex nofollow\">the most mature agentic AI platform in the enterprise CRM space as of 2026<\/a>, with the Atlas Reasoning Engine enabling multi-agent coordination. Setup complexity and licensing cost make it impractical for many teams under 50 reps. <strong>HubSpot Breeze<\/strong> combines a copilot, autonomous agents, and enrichment intelligence, yet <a href=\"https:\/\/creatio.com\/glossary\/ai-crm\" target=\"_blank\" rel=\"noindex nofollow\">platforms such as HubSpot primarily offer AI assistants or copilots that still require human initiation for most actions<\/a>. <strong>monday CRM<\/strong> excels at outbound sequence automation but does not offer a companion mode for existing CRM stacks. <strong>Pipedrive<\/strong> <a href=\"https:\/\/pandadoc.com\/blog\/top-ai-crm-systems\" target=\"_blank\" rel=\"noindex nofollow\">does not offer AI agents and instead provides an AI Sales Assistant limited to recommendations<\/a>. <strong>Creatio<\/strong> offers genuine no-code agentic architecture at a competitive price point but lacks a companion deployment model. <strong>Coffee<\/strong> is the only platform that operates as both a standalone agent-first CRM and a companion layer that writes back to Salesforce or HubSpot with deep integration awareness of required fields, forecasting, and quota logic.<\/p>\n<h2>Best CRM for Eliminating Manual Data Entry: Category-by-Category Breakdown<\/h2>\n<p><strong>Data Capture.<\/strong> Manual CRM data entry consumes a large share of the sales workweek because most systems wait for a human to type. Coffee\u2019s agent connects to Google Workspace or Microsoft 365 and scans emails and calendars to auto-create contacts, companies, and activity records. Reps avoid field-by-field entry. Salesforce Agentforce can qualify inbound leads and book meetings autonomously, but <a href=\"https:\/\/pandadoc.com\/blog\/top-ai-crm-systems\" target=\"_blank\" rel=\"noindex nofollow\">Salesforce distinguishes its generative AI capabilities from Agentforce autonomous execution<\/a>, and the two layers require separate configuration. HubSpot Breeze Intelligence handles enrichment and buying-signal detection, but reps still manually trigger most record updates.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678186019-5cc1a76ac78e.gif\" alt=\"Build people lists automatically with Coffee AI CRM Agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Build people lists automatically with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<p><strong>Activity Logging.<\/strong> Even when contact data is captured automatically, reps face a second manual burden: logging every call, email, and meeting. Manual CRM activity logging can take up to an hour per day. Coffee logs last activity and next activity autonomously after every email, call, and meeting. Creatio\u2019s pre-built agents handle meeting prep and follow-ups autonomously. Pipedrive requires manual entry for all activity logging, and its AI layer surfaces suggestions only.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678321672-5c8717cf0024.gif\" alt=\"Create instant meeting follow-up emails with the Coffee AI CRM agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Create instant meeting follow-up emails with the Coffee AI CRM agent<\/em><\/figcaption><\/figure>\n<p><strong>Workflow Execution.<\/strong> Shadow CRMs, such as Notion pages and spreadsheets, appear when CRM adoption collapses and reps find it faster to maintain their own records. <a href=\"https:\/\/zoom.com\/en\/blog\/what-is-ai-agent\" target=\"_blank\" rel=\"noindex nofollow\">The shift from reactive to proactive resolution separates agentic AI from the automation tools many CRM platforms already use<\/a>. Coffee\u2019s agent drafts post-call summaries, identifies next steps, and queues follow-up emails for one-click send. This flow removes the post-meeting administrative block that drives shadow CRM behavior.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678549697-4e8d65abe17d.gif\" alt=\"GIF of Coffee platform where user is using AI to prep for a meeting with Coffee AI\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Automated meeting prep with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<h2>HubSpot Breeze vs Salesforce Agentforce Task Automation: Where the Gaps Remain<\/h2>\n<p>Both platforms invest heavily in agentic language, yet the execution gaps remain significant for small and mid-market sales teams.<\/p>\n<p>Salesforce Agentforce <a href=\"https:\/\/aimultiple.com\/agentic-crm\" target=\"_blank\" rel=\"noindex nofollow\">reorganized Sales Cloud around autonomous agents rather than cloud products<\/a> and supports multi-agent coordination via the Atlas Reasoning Engine. In practice, deploying Agentforce requires Salesforce admin resources, Flow configuration, and Data Cloud licensing that most sub-100-rep teams do not have. The autonomous execution is real, and the implementation barrier is equally real.<\/p>\n<p>HubSpot Breeze <a href=\"https:\/\/aimultiple.com\/agentic-crm\" target=\"_blank\" rel=\"noindex nofollow\">upgraded to GPT-5 and expanded support for nine channels including SMS, WhatsApp, Instagram, and Slack<\/a>. The Prospecting Agent gathers lead information and personalizes outreach autonomously. As noted in the comparison above, the Breeze Copilot layer, which handles email drafting and record summarization, is the feature most reps encounter daily, not the autonomous agent tier.<\/p>\n<p>Both platforms deliver autonomous execution in theory, yet both either require enterprise IT resources, in the case of Salesforce, or default to human-initiated actions, in the case of HubSpot. For teams already committed to either platform, Coffee\u2019s Companion App addresses this implementation gap directly. The Coffee Agent handles the data-in process, capturing calls, enriching contacts, logging activities, and writing clean, structured data back to the existing Salesforce or HubSpot instance without disrupting required fields or forecasting logic.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Get started with Coffee as a companion layer for your existing CRM \u2192<\/a><\/p>\n<h2>Standalone vs. Companion: Team-Size Recommendations<\/h2>\n<p><strong>1\u201320 reps, no existing CRM contract.<\/strong> Coffee Standalone fits teams that want an agent-managed system of record from day one. No Salesforce admin and no HubSpot onboarding are required. Connect Google Workspace or Microsoft 365 and the agent begins populating contacts and pipeline immediately.<\/p>\n<p><strong>5\u2013100 reps, committed to Salesforce or HubSpot.<\/strong> Coffee Companion fits teams that want autonomous data quality without a migration. Authenticate once and the agent syncs, enriches, and writes insights back to the primary CRM. Teams in this range face the highest cost of bad data, and <a href=\"https:\/\/pipeline.zoominfo.com\/sales\/ai-sales-automation-software\" target=\"_blank\" rel=\"noindex nofollow\">sales reps who use AI effectively are 3.7x more likely to hit quota<\/a>. The Companion model delivers that benefit while preserving the existing stack.<\/p>\n<p><strong>Legacy-committed teams evaluating stack consolidation.<\/strong> Coffee Companion replaces point solutions for enrichment, such as Apollo and ZoomInfo, conversation intelligence, such as Gong, and pipeline reporting based on manual CSV exports. This shift reduces both cost and the tool-switching that fragments rep attention.<\/p>\n<p>Newer AI-native CRMs such as Day.ai and Clarify address the passive-database problem but lack the integration depth required for established Salesforce and HubSpot environments. They often miss awareness of quotas, forecasting models, required fields, and custom objects. Coffee\u2019s Companion model treats that complexity as a first-class constraint.<\/p>\n<h2>Operational Considerations, Risks, and Common Misconceptions<\/h2>\n<p><strong>Implementation time.<\/strong> Coffee connects via OAuth to Google Workspace or Microsoft 365. For the Standalone model, a 10-rep team becomes operational within a day. The Companion model requires a single authentication to Salesforce or HubSpot, and field mapping plus write-back configuration add time proportional to CRM complexity.<\/p>\n<p><strong>Data security.<\/strong> Coffee is SOC 2 Type 2 and GDPR compliant. Data captured by the agent is not used to train public models. <a href=\"https:\/\/teradata.com\/insights\/ai-and-machine-learning\/what-is-autonomous-ai\" target=\"_blank\" rel=\"noindex nofollow\">Autonomous AI requires strong governance including role-based access, audit trails, and human oversight modes<\/a>. Coffee\u2019s architecture incorporates human-in-the-loop review for outbound actions such as follow-up emails, which are queued for rep approval before sending.<\/p>\n<p><strong>Data quality vs. dedicated enrichment tools.<\/strong> Coffee\u2019s built-in enrichment, including job titles, funding data, and LinkedIn profiles via licensed data partners, matches Apollo for most use cases. Teams with specialized enrichment requirements in highly regulated or niche verticals should evaluate coverage depth before consolidating.<\/p>\n<p><strong>Integration breadth.<\/strong> Current third-party integrations run via Zapier, and deeper native integrations sit on the roadmap. Teams with complex multi-tool stacks should map critical workflow dependencies before migrating.<\/p>\n<p><strong>Who Coffee is not for.<\/strong> Large enterprises with hundreds of custom Salesforce objects, healthcare or finance organizations requiring multi-year security reviews, and teams seeking a static feature-checklist database rather than an autonomous agent fall outside Coffee\u2019s current ICP.<\/p>\n<h2>Practical Decision Framework: Matching Your Constraints to the Right Option<\/h2>\n<p>Use the table below to match your team\u2019s size, contracts, and primary constraints to the platform setup that fits best. Treat it as a shortcut to narrow options before a deeper evaluation.<\/p>\n<table>\n<thead>\n<tr>\n<th>Constraint<\/th>\n<th>Best-Fit Option<\/th>\n<th>Why<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>No CRM, 1\u201320 reps, want zero admin<\/td>\n<td>Coffee Standalone<\/td>\n<td>Agent manages system of record from day one, no configuration overhead<\/td>\n<\/tr>\n<tr>\n<td>Salesforce\/HubSpot contract, poor data quality<\/td>\n<td>Coffee Companion<\/td>\n<td>Agent writes clean data back without disrupting existing workflows<\/td>\n<\/tr>\n<tr>\n<td>Need autonomous agents and no-code builder, mid-market<\/td>\n<td>Creatio<\/td>\n<td>Architecture-level AI with drag-and-drop workflow builder, no companion mode<\/td>\n<\/tr>\n<tr>\n<td>Enterprise, large Salesforce footprint, IT resources available<\/td>\n<td>Salesforce Agentforce<\/td>\n<td>Mature agentic platform, high implementation cost justified at scale<\/td>\n<\/tr>\n<tr>\n<td>HubSpot-committed, need enrichment and outreach automation<\/td>\n<td>HubSpot Breeze + Coffee Companion<\/td>\n<td>Breeze handles marketing automation, Coffee fills autonomous data-capture gap<\/td>\n<\/tr>\n<tr>\n<td>Outbound-first, sequence automation priority<\/td>\n<td>monday CRM or Coffee Standalone<\/td>\n<td>monday excels at sequence management, Coffee adds autonomous logging and pipeline intelligence<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does Coffee implementation typically take for a 10-rep team?<\/h3>\n<p>For the Standalone CRM, a 10-rep team connects Google Workspace or Microsoft 365 via OAuth and the Coffee Agent begins populating contacts, companies, and activity records immediately. Most teams are fully operational within one business day. There is no CRM migration, no field-mapping exercise, and no admin configuration required to start capturing data. For the Companion App on top of Salesforce or HubSpot, implementation time depends on the complexity of the existing instance, specifically the number of required fields, custom objects, and forecasting configurations the agent must respect. Simple HubSpot instances typically go live within a day or two. More customized Salesforce environments may require a short scoping session to map write-back rules correctly.<\/p>\n<h3>Can the Coffee Agent write back to an existing Salesforce instance without breaking required fields or forecasting?<\/h3>\n<p>Yes. Coffee\u2019s Companion App was built with Salesforce\u2019s required-field logic, quota structures, and forecasting categories as first-class constraints. When the agent writes enriched contact data, activity logs, or pipeline updates back to Salesforce, it respects the validation rules and field dependencies already configured in the instance. This design differs from newer AI-native CRMs that lack deep Salesforce integration experience. The agent does not overwrite fields with null values, bypass required-field validation, or alter forecast categories without explicit configuration. Teams can also use Coffee\u2019s API access to script custom prompts that pull from Coffee\u2019s data warehouse for bespoke briefings without touching Salesforce\u2019s core data model.<\/p>\n<h3>What SOC 2 and GDPR controls protect data when Coffee acts as a companion layer?<\/h3>\n<p>Coffee is SOC 2 Type 2 certified and GDPR compliant. In companion-layer mode, the agent reads from and writes to your Salesforce or HubSpot instance using OAuth-scoped credentials. It does not store credentials and does not retain a separate copy of your CRM data outside of the Coffee data warehouse, which is used exclusively to power pipeline intelligence features like Pipeline Compare. Data processed by the Coffee Agent is not used to train public AI models. Role-based access controls ensure that the agent operates within the permission boundaries of the authenticated user. Teams in regulated industries that require multi-year security reviews or custom data processing agreements should compare Coffee\u2019s current compliance posture with their specific requirements before deployment.<\/p>\n<h3>How do I test whether an AI CRM is truly autonomous versus rule-based before committing?<\/h3>\n<p>Run a two-week pilot with zero manual CRM input from your reps. Connect the platform to your email and calendar, conduct normal sales activity such as calls, emails, and meetings, and then audit the CRM at the end of week two. A truly autonomous agent will have created contact records, logged every interaction, enriched company data, and updated deal stages without a single manual entry. A rule-based or copilot-tier tool will show empty fields, missing activities, or records that only updated when a rep clicked a suggestion. Test whether call transcripts are automatically converted into structured BANT or MEDDIC fields, whether follow-up emails are drafted and queued without rep initiation, and whether pipeline changes are tracked week over week without a manual export. If you have an existing Salesforce or HubSpot instance, also test the companion scenario and verify that the agent writes back correctly to required fields and does not create duplicate records or break validation rules.<\/p>\n<h2>Conclusion: Choosing the CRM That Actually Removes Data Chores<\/h2>\n<p>Legacy CRMs still behave like passive databases that turn sales reps into data-entry clerks. Market data shared by Coffee shows that 71% of sales reps say they spend too much time on data entry, leaving only 35% of their time for selling. Platforms that solve this problem in 2026 rely on autonomous agent architectures that perceive, reason, and act without waiting for a human to open a record and type.<\/p>\n<p>Coffee is the only platform that delivers this architecture in two deployment models. It functions as a standalone agent-first CRM for teams of 1\u201320 reps who want to skip the passive-database era entirely. It also operates as a companion layer for Salesforce and HubSpot teams that need autonomous data quality without a platform migration. The Pipeline Compare feature, visitor identification with Suggested Leads, and built-in enrichment consolidate the point-solution stack that fragments rep attention and inflates RevOps costs.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">Get started with Coffee, a CRM with AI task automation that removes manual data chores \u2192<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Coffee saves sales teams 8\u201312 hrs\/week with fully autonomous AI task automation. See how it outperforms every major CRM. Start free today.<\/p>\n","protected":false},"author":11,"featured_media":7491,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-7492","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/7492","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/comments?post=7492"}],"version-history":[{"count":0,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/7492\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/7491"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=7492"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=7492"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=7492"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}