{"id":23556,"date":"2026-06-25T06:00:17","date_gmt":"2026-06-25T06:00:17","guid":{"rendered":"https:\/\/engineerbabu.com\/blog\/?p=23556"},"modified":"2026-06-25T06:00:17","modified_gmt":"2026-06-25T06:00:17","slug":"build-a-multi-agent-ai-system","status":"publish","type":"post","link":"https:\/\/engineerbabu.com\/blog\/build-a-multi-agent-ai-system\/","title":{"rendered":"How to Build a Multi-Agent AI System &#8211; Orchestration, Memory, Tool Use, Production Deployment 2026"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">A single <\/span><a href=\"https:\/\/engineerbabu.com\/services\/ai-development\"><span style=\"font-weight: 400;\">AI agent<\/span><\/a><span style=\"font-weight: 400;\"> is powerful. A system of coordinated AI agents that can decompose complex goals, delegate to specialists, share context, and recover from individual failures is transformative.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Multi-agent systems are the architecture that makes enterprise AI automation possible at scale. <\/span>According to <a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027\" target=\"_blank\" rel=\"noopener\">Gartner<\/a>, by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from virtually 0% in 2024.<\/p>\n<h2><b>What Is a Multi-Agent AI System?<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Most AI applications begin with a single large language model handling an entire request. While this works well for straightforward tasks, enterprise workflows are rarely simple. They involve research, planning, calculations, document generation, approvals, <\/span><a href=\"https:\/\/engineerbabu.com\/blog\/healthcare-api-integration-use-cases\/\"><span style=\"font-weight: 400;\">API integrations<\/span><\/a><span style=\"font-weight: 400;\">, and continuous monitoring, all of which require different types of expertise.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A multi-agent AI system addresses this by breaking a complex objective into smaller tasks handled by specialized AI agents. Instead of one general-purpose agent attempting everything, an orchestrator coordinates multiple agents that collaborate, share information, use approved tools, and combine their outputs into a single result.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A typical enterprise multi-agent workflow includes:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Understanding the user&#8217;s objective and creating an execution plan.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Assigning specialized tasks to dedicated AI agents.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Running independent tasks in parallel wherever possible.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sharing relevant context through a centralized memory layer.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Calling business applications and APIs through secure tool access.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Recovering automatically if an individual agent encounters an error.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tracking every decision, action, and AI interaction for auditing and optimization.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This architecture enables organizations to automate complex business processes while improving reliability, scalability, and operational transparency.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-23561\" src=\"https:\/\/engineerbabu.com\/blog\/wp-content\/uploads\/2026\/06\/1_distributed_trace_dashboard.png\" alt=\"\" width=\"1900\" height=\"1200\" title=\"\"><\/p>\n<h2><b>Module 1 &#8211; Why Multi-Agent Instead of Single Agent<\/b><\/h2>\n<p><b>Single agent limitations:<\/b><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Limitation<\/b><\/td>\n<td><b>Impact<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Context window constraints<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Complex tasks exceed what fits in one agent&#8217;s window<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Specialisation vs generality<\/span><\/td>\n<td><span style=\"font-weight: 400;\">A generalist agent performs mediocrely across all tasks<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Sequential execution<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cannot run parallel tasks simultaneously<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Fault isolation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Single agent failure terminates the whole task<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Multi-agent advantages:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Specialisation: research agent + coding agent + writing agent each excel<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Parallelism: multiple subtasks run simultaneously<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Context management: each agent maintains own window<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Independent failure recovery<\/span><\/li>\n<\/ul>\n<h2><b>Module 2 &#8211; Agent Design Patterns<\/b><\/h2>\n<ul>\n<li aria-level=\"1\"><b>Pattern 1 &#8211; Orchestrator-Worker:<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Orchestrator receives the high-level goal, delegates subtasks to specialist workers, tracks progress, handles failures, assembles final output.<\/span><\/p>\n<ul>\n<li aria-level=\"1\"><b>Pattern 2 &#8211; Pipeline:<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Agents form a sequential chain. Each transforms the previous agent&#8217;s output. Used for well-defined linear workflows.<\/span><\/p>\n<ul>\n<li aria-level=\"1\"><b>Pattern 3 &#8211; Peer Collaboration:<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Multiple specialist agents review each other&#8217;s outputs and refine them. Used for quality assurance, writing agent produces draft, critic agent identifies weaknesses, writing agent revises.<\/span><\/p>\n<ul>\n<li aria-level=\"1\"><b>Pattern 4 &#8211; Debate:<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Multiple agents independently generate solutions. A judge agent evaluates and selects the best. Used where correctness is verifiable.<\/span><\/p>\n<h2><b>Module 3 &#8211; Tool Registry with Guardrails<\/b><\/h2>\n<p><b>Standard tool interface:<\/b><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Field<\/b><\/td>\n<td><b>Description<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Name<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Unique identifier (e.g., get_crm_contact)<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Description<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Natural language for LLM to understand when to use it<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Input schema<\/span><\/td>\n<td><span style=\"font-weight: 400;\">JSON Schema defining parameters<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Output schema<\/span><\/td>\n<td><span style=\"font-weight: 400;\">JSON Schema defining response<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Permissions<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Which agents\/users can call this tool<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Rate limits<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Maximum calls per minute\/hour<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Audit flag<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Does this tool write data? (triggers human approval)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Tool access matrix:<\/b><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Tool<\/b><\/td>\n<td><b>Orchestrator<\/b><\/td>\n<td><b>Research Agent<\/b><\/td>\n<td><b>Writing Agent<\/b><\/td>\n<td><b>Financial Agent<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Web search<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u2705<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u2705<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u2705<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Database write<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u2705<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Email send<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u2705<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Code execution<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u274c<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u2705<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><b>Module 4 &#8211; Shared Memory Architecture<\/b><\/h2>\n<p><b>Memory types:<\/b><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Type<\/b><\/td>\n<td><b>Scope<\/b><\/td>\n<td><b>Implementation<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Working memory<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Current task<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Redis key-value store keyed by task_id<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Episodic memory<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Past task history<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Vector database with task summaries<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Semantic memory<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Domain knowledge<\/span><\/td>\n<td><span style=\"font-weight: 400;\">RAG knowledge base<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Procedural memory<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Learned workflows<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Prompt templates updated from feedback<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><b>Working memory structure per task:<\/b><\/p>\n<p><span style=\"font-weight: 400;\">{<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0&#8220;task_id&#8221;: &#8220;task_xyz789&#8221;,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0&#8220;goal&#8221;: &#8220;Competitive intelligence report on CompanyX&#8221;,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0&#8220;status&#8221;: &#8220;in_progress&#8221;,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0&#8220;agent_outputs&#8221;: {<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0&#8220;research_agent&#8221;: {&#8220;status&#8221;: &#8220;completed&#8221;, &#8220;output&#8221;: {}},<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0&#8220;financial_agent&#8221;: {&#8220;status&#8221;: &#8220;in_progress&#8221;}<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0},<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0&#8220;shared_findings&#8221;: {<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0&#8220;company_name&#8221;: &#8220;CompanyX&#8221;,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0&#8220;founded&#8221;: 2018<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0}<\/span><\/p>\n<p><span style=\"font-weight: 400;\">}<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-23558\" src=\"https:\/\/engineerbabu.com\/blog\/wp-content\/uploads\/2026\/06\/5_shared_memory_architecture.png\" alt=\"\" width=\"1700\" height=\"850\" title=\"\"><\/p>\n<h2><b>Module 5 &#8211; Observability, Tracing, and Cost Management<\/b><\/h2>\n<p><b>The distributed trace:<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Every task execution produces a complete immutable log of every agent call, tool call, message, and decision with timestamps.<\/span><\/p>\n<p><b>The trace view (Gantt chart):<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Task: Competitive Intelligence Report<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u251c\u2500 Orchestrator (plan): 2.3s<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u251c\u2500 Research Agent (parallel):<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u2502\u00a0 \u251c\u2500 web_search(&#8220;CompanyX products&#8221;): 1.2s<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u2502\u00a0 \u2514\u2500 Total: 8.4s<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u251c\u2500 Financial Agent (parallel): 5.1s<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u2514\u2500 Writing Agent: 12.1s<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Total: 21.4s | Cost: $0.047<\/span><\/p>\n<p><b>Cost management:<\/b><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Each LLM call logs:<\/strong> model, input tokens, output tokens, cost. <strong>Budget limits configured at:<\/strong> per tool call, per agent, per task, per user\/team. Daily budget limits prevent runaway costs.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-23559\" src=\"https:\/\/engineerbabu.com\/blog\/wp-content\/uploads\/2026\/06\/3_orchestrator_worker_architecture.png\" alt=\"\" width=\"1700\" height=\"1000\" title=\"\"><\/p>\n<h2><b>Cost to\u00a0<\/b><strong><b>Build a Multi-Agent AI System<\/b><\/strong><\/h2>\n<table>\n<tbody>\n<tr>\n<td><b>Module<\/b><\/td>\n<td><b>Cost Range (USD)<\/b><\/td>\n<td><b>Notes<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Agent runtime (per agent type)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$4K \u2013 $8K per agent<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~5 specialist agents initially<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Orchestrator with planning<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$8K \u2013 $15K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Inter-agent communication layer<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$6K \u2013 $12K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Shared memory (Redis + vector DB)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$5K \u2013 $10K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Tool registry + access control<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$6K \u2013 $12K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Rate limiting + budget enforcement<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$4K \u2013 $8K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Distributed tracing system<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$8K \u2013 $15K<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Full task trace<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Failure recovery + retry logic<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$5K \u2013 $10K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Cost tracking + analytics<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$4K \u2013 $8K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Human approval gates<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$4K \u2013 $8K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">AWS + security + VAPT<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$5K \u2013 $10K<\/span><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td><b>Total<\/b><\/td>\n<td><b>$79K \u2013 $156K<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Full multi-agent system<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">Contact: <\/span><a href=\"mailto:mayank@engineerbabu.com\"><b>mayank@engineerbabu.com<\/b><\/a><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-23560\" src=\"https:\/\/engineerbabu.com\/blog\/wp-content\/uploads\/2026\/06\/2_orchestration_control_panel.png\" alt=\"\" width=\"1900\" height=\"1075\" title=\"\"><\/p>\n<h1><b>Conclusion<\/b><\/h1>\n<p><span style=\"font-weight: 400;\">Enterprise AI delivers the greatest value when multiple specialized agents work together instead of relying on a single general-purpose model. By combining orchestration, shared memory, secure tool access, observability, and intelligent cost management, multi-agent systems can automate complex workflows with greater accuracy, resilience, and scalability.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Whether you&#8217;re building AI copilots for employees, automating cross-functional business processes, or deploying autonomous enterprise workflows, a well-designed multi-agent architecture provides the foundation for reliable AI at scale.<\/span><\/p>\n<p><a href=\"http:\/\/engineerbabu.com\"><span style=\"font-weight: 400;\">EngineerBabu<\/span><\/a><span style=\"font-weight: 400;\"> specializes in designing and developing enterprise-grade multi-agent AI systems that integrate with your existing applications, data sources, and business workflows.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">From architecture design and custom agent development to secure deployment and ongoing optimization, our team can help you build production-ready AI automation tailored to your organization&#8217;s needs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ready to build an enterprise multi-agent AI system? Contact EngineerBabu to discuss your AI automation requirements.<\/span><\/p>\n<h2><b>Frequently Asked Questions<\/b><\/h2>\n<ul>\n<li aria-level=\"1\">\n<h3><b>What is the orchestrator-worker pattern and when should it be used?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The orchestrator-worker pattern uses a central orchestrator agent to receive high-level goals, decompose them into subtasks, delegate to specialist worker agents, monitor progress, handle failures, and assemble final outputs. It is the right pattern when: tasks require multiple types of expertise, subtasks can run in parallel, and the task structure is discoverable from the goal. It works best when subtasks have clear input\/output contracts and when failures in individual workers can be isolated without restarting the entire task.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h3><b>How does cost management work in a multi-agent system?<\/b><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Each LLM call is logged with model name, input token count, output token count, and calculated cost. The platform aggregates cost at four levels: per tool call, per agent execution, per task, and per user\/team. Budget limits can be configured at each level, a task budget of $0.50 stops execution when reached and returns a partial result. Cost analytics show which agents and task types consume the most budget, enabling model substitution decisions, replacing GPT-4o with GPT-4o-mini for lower-value subtasks reduces costs 10x with minimal quality impact.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A single AI agent is powerful. A system of coordinated AI agents that can decompose complex goals, delegate to specialists, share context, and recover from individual failures is transformative. Multi-agent systems are the architecture that makes enterprise AI automation possible at scale. According to Gartner, by 2028, at least 15% of day-to-day work decisions will [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":23557,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1246],"tags":[],"class_list":["post-23556","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-healthtech"],"_links":{"self":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/23556","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/comments?post=23556"}],"version-history":[{"count":1,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/23556\/revisions"}],"predecessor-version":[{"id":23562,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/23556\/revisions\/23562"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/media\/23557"}],"wp:attachment":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/media?parent=23556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/categories?post=23556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/tags?post=23556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}