By Thomas Benjamin, SVP, Product Development & Engineering, Boomi
Most companies have treated AI as a chatbot or copilot. That phase is over. In 2027, we move from AI that answers questions to agents that get work done, and software stops waiting for a click. I have always believed in the art of the possible: technology that solves hard problems and delivers measurable results. Technology for its own sake has zero value. The agentic world rewards companies that align people, process, and architecture so customers see real returns.
Five Technology Trends Defining 2027
Specialized Agents Inside a Harness
One giant agent that does everything loses track of details and gets less reliable with every step. The pattern that works is small agents, each doing one job well, inside a harness. The harness is the runtime around the models. It routes work, manages tools and permissions, checkpoints progress so a failed run resumes, and verifies output before it is committed.
Forrester expects the harness, not the model, to drive three quarters of AI platform decisions in 2027. I agree.
Value to customers: reliable results from long-running work, and self-healing operations that fix routine issues before anyone files a ticket. Our customers should never be our detection mechanism.
Plug-and-Play Connections
At Boomi, we have connected systems for more than twenty years, and it used to take months of custom code. Open standards like the Model Context Protocol change that. An agent plugs into databases, ERPs, and cloud tools in hours, and a tool built once serves every agent that follows. The gateway in front of them is where access is authorized, usage metered, and every call audited.
Value to customers: faster time to value, with weeks of handoffs collapsing into hours and no lock-in to a single vendor.
Identity, Guardrails, and a Kill Switch
Every agent gets its own ID badge, sees only permitted data, has every action logged, and can be switched off in seconds. Gartner expects agent abuse to cause a quarter of enterprise breaches by 2028, and 82% of organizations surveyed have found agents they did not know existed. Hard dates arrive in 2027: U.S. state laws take effect on January 1, and high-risk duties under the EU AI Act begin on December 2. If you cannot say who owns an agent, what it may touch, and how to turn it off, fix that first.
Value to customers: a smaller blast radius when something goes wrong, audit evidence regulators and insurers ask for, and faster approvals because trust is built in.
Right-Sized Context and Models
Dumping millions of raw documents into a model makes it slow, expensive, and wrong. Give agents exactly the clean, governed data they need at the moment of decision. Gartner estimates that it lifts accuracy by up to 80% and cuts cost by up to 60%. Right-size the model too: small, task-specific models handle the repetitive steps, and Gartner expects them to be used three times more than general-purpose LLMs in 2027, with frontier models kept for planning and verification.
Value to customers: higher accuracy at predictable cost, with sensitive workloads kept close to the data.
Cost Measured by Outcomes
Agents consume tokens by decision, not by seat. IDC predicts 55% of enterprises will see agent bills rise with no price change by the end of 2027, because they track tokens, not value. We measure teams on their say/do ratio; agents deserve the same discipline. Give every agent a budget and an owner, measure the cost of a completed, accurate outcome, and treat a token spike as an incident; a runaway loop and an attack look the same on the invoice.
Value to customers: AI spend you can defend in a budget cycle, measured against the process it replaced.
Why This Matters
Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027 because of cost, unclear value, or weak risk controls. This is not a story about careless teams. Agents were bolted onto workflows nobody redesigned or they were rushed into production without guardrails; McKinsey finds workflow redesign is the strongest predictor of impact. Speed without control is a risk, so every agent climbs a trust ladder.
- Shadow mode: the agent observes and suggests, touching nothing in production.
- Supervised mode: the agent acts only after a human approves.
- Autonomous mode: earned after thousands of verified runs.
When agents take the repetitive work, our teams get their time back for customers and the hard problems. The winners of 2027 will keep architecture simple, govern every agent, and prove value in outcomes customers can feel. The Ask: pick one workflow, put one agent on the trust ladder, and measure cost per outcome from day one. Ship fast. Prove it works. Own the outcome. Let’s go build it.
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ABOUT THE AUTHOR
Thomas Benjamin is a technology leader dedicated to delivering exponential customer value, guided by the principle that every $100 investment in technology must generate at least $5,000 in sustaining value for customers. As Senior Vice President of Product Development & Engineering at Boomi, Thomas leads the Global Software Engineering and Cloud operations teams, responsible for scaling and building intelligent, AI Agent-powered solutions for over 30,000+ customers.
Thomas is a recognized Subject Matter Expert in AI Agents, Microservices, SaaS architecture, and extreme-scale cloud systems. With over 25 years of experience, he specializes in building software platforms powered by AI Agents, MCP tools, robust APIs, and data activation.
Previously, as EVP and CTO at NI (Emerson), Thomas led the transformation of the test and measurement industry through generative AI and data analytics. His distinguished career includes serving as CTO and SVP of Engineering at SAP Ariba, and holding executive leadership roles at General Electric, Emirates Group, Visa, Walmart, and Oracle.
Thomas holds a Master of Science in Industrial Engineering from the University of Cincinnati and a Bachelor’s in Engineering from The College of Engineering Guindy, Anna University, India. He also holds patents in Blockchain-enabled supply chain provenance and maintains executive certifications from Stanford, Singularity, and Harvard Business School Online.
