Recently, Tera delivered benchmark-leading performance across four public benchmarks, using the same model and same task as other AI coding assistants and enterprise data coworkers. Results included:
- 58% lower cost
- 42% faster execution
- 73% fewer tokens
- Top Pass³ performance and benchmark-leading accuracy
It raises an interesting question: Why do different AI systems using the same model and task produce such different results?
We built Tera because enterprise data work needs a different kind of AI. Data work is messy. Context is fragmented. Workflows are long-running. Governance matters. And a wrong answer often looks exactly like a right one.
The benchmark results reinforced something we suspected from the beginning.
The model matters. But the execution architecture increasingly matters more.
What happens before the model is called, how work is planned, how context is assembled, how tools are coordinated, and how tasks are carried through to completion can make an enormous difference.
Behind every great coworker is mission control
Think about the Apollo or Artemis II missions. Behind those astronauts is an entire mission control room helping ensure the mission succeeds.
Planning. Monitoring. Coordinating. Recovering from problems. Making sure every step happens in the right sequence.
Enterprise AI works much the same way. When most people think about AI, they think about the model. But increasingly, the model is just one component. What's becoming far more important is the execution layer behind it: the context, memory, orchestration, guardrails, planning, recovery, and runtime controls that help AI move from generating answers to completing work.
That's why we believe the next evolution of enterprise AI isn't just a better assistant or a more capable model.
It's a coworker.
AI is evolving from answers to outcomes
Enterprise AI has progressed through several generations in a remarkably short period of time:
- Chatbots answered questions
- Copilots helped users perform tasks
- Agents began executing multistep workflows
Now we're entering the era of agentic coworkers.
A coworker participates differently than an assistant. When you ask a colleague to investigate declining customer retention, you don't provide a twenty-step playbook covering every spreadsheet, dashboard, meeting, and analysis they should perform. You describe the problem and the goal. The expectation is that they'll draw on their expertise, adapt as they learn new information, and help get the work to a useful result.
That same expectation is beginning to emerge with AI. Valuable enterprise work often requires gathering information from multiple sources, interpreting results, coordinating actions across systems and teams, incorporating feedback, and adjusting course when conditions change.
The shift from generating answers to helping achieve outcomes is what defines the emerging coworker era.
4 things a real agentic coworker delivers
A true agentic coworker should make data work better in four ways.
1. Economics
Every model call costs money. Every unnecessary tool invoked costs money. Every extra turn spent rediscovering context costs money. Many AI systems repeatedly perform the same reasoning, retrieve the same information, and rebuild the same context.
Tera is designed to reduce that waste.
It batches work, preserves context across turns, uses purpose-built skills, routes work intelligently, and leverages the most appropriate execution path for the task.
The result is that more of your AI spend goes toward completing useful work instead of repeatedly figuring out what work needs to be done.
2. Accuracy and reliability
Using AI for enterprise data work can be deceptively difficult.
A customer segment might exclude a region because of an incorrect join. A forecast might ignore an exception that everyone in the company knows about but nobody wrote down. The chart still looks reasonable, and the answer still feels believable. In many cases, that's what makes the problem dangerous.
That's why Tera works from governed enterprise knowledge, business relationships, definitions, policies, and evidence rather than relying solely on whatever happens to be included in a prompt.
3. Speed
Most of us have worked on projects that seemed 90% complete three different times before they were actually finished.
Tera helps compress those timelines. Work that once required days or weeks of effort can often begin with a conversation and produce a useful result in minutes.
That applies across a surprising range of work, whether it's investigating a customer issue, building a forecast, optimizing a data pipeline, creating a model, or segmenting customers.
Behind the scenes, Tera keeps the work moving forward even when the path isn't perfectly linear.
4. Convenience
Most enterprises already have data, tools, processes, and governance spread across multiple environments.
The last thing they want is another platform that requires them to move data, standardize on a single model, or rebuild workflows inside a new ecosystem.
Tera takes a different approach.
It runs where data work already happens. Instead of forcing organizations to bring their data to AI, Tera helps bring AI closer to governed enterprise data. Organizations can connect existing tools and systems, deploy across cloud, hybrid, on-premises, sovereign, and air-gapped environments, and choose the models that make the most sense for a particular job.
Not every task requires the reasoning power and cost of a frontier model. Tera can reserve expensive frontier models for the problems that genuinely benefit from them, while routing simpler tasks to smaller models, open-weight models, specialized skills, in-database analytics, or other lower-cost execution paths. The result is that organizations can scale AI usage without scaling cost at the same rate.
As new models and AI technology emerge, enterprises should be able to adopt them without redesigning their AI strategy or being tied to a particular model provider or ecosystem.
Who benefits?
The value of an agentic coworker shows up at multiple levels.
Individuals
At the individual level, a coworker expands access to expertise.
For decades, extracting maximum value from enterprise data platforms often required significant technical knowledge. Users needed to know where data lived, how tables related to one another, which analytical methods to apply, how to write SQL, and how to troubleshoot the workflow when something went wrong.
Consider a customer segmentation project.
In the past, a user would need to locate relevant tables, prepare data, normalize variables, choose the appropriate analytical technique, write queries, execute workflows, troubleshoot issues, and interpret results.
With Tera, the user can begin with a business objective: “Segment my customers into meaningful groups.”
Tera helps discover relevant data, prepare it, select the appropriate analytics, execute the workflow, and deliver results.
There's an important distinction here.
Tera isn't replacing expertise. It's helping skilled people spend less time wrestling with the plumbing and more time applying their expertise to the outcome. So people can spend less time assembling workflows, finding information, moving data between systems, and troubleshooting routine tasks, and more time solving business problems.
Teams
Data engineers can diagnose bottlenecks faster. Data scientists can accelerate preparation and model workflows. AI engineers can focus on outcomes instead of infrastructure. Analysts and business users can explore data through natural language. Platform administrators can proactively optimize and manage operations.
Organizations
Organizations ultimately care about outcomes and economics. If AI can deliver more work, greater reliability, and better accuracy at the same spend, the result is more outcomes from the same investment.
How to evaluate an agentic coworker
As the market shifts from assistants to agentic coworkers, enterprises should ask a different set of questions.
What does a successful outcome actually cost?
Token counts are easy to measure. The more important metric is the cost of achieving a reliable outcome. How many turns did it take? How many retries? How much human intervention? How much rework?
Does it understand your business or just your data?
Many systems can retrieve a schema. Far fewer understand how the business defines revenue, customer, risk, margin, or churn. Business context often determines whether an answer is useful, misleading, or wrong.
Who owns the execution layer?
Models will change. The execution layer is what determines how context is assembled, how tools are called, how policies are enforced, and how work is completed. Can you inspect it? Can you control it? Can you evolve it? Or are you renting a black box?
The future belongs to coworkers
Enterprises all have access to the same frontier models. That reality changes the competitive landscape.
The future of enterprise AI will not be decided solely by who has the best model. It will be determined by who can most effectively combine business context, specialized expertise, intelligent execution, and governance to turn intent into outcomes.
In other words: The future belongs to coworkers.
And the organizations that learn how to deploy them effectively will spend less time asking AI for answers and more time using AI to get work done.
To explore the benchmark methodology, results, and architectural principles behind these outcomes, see Tera's benchmark report and harness architecture articles.