Abstract representation of Tera Harness featuring a cropped portion of the Tera symbol

Tera Harness for enterprise data work

Tera Harness is the execution engine behind Tera, Teradata's agentic coworker for enterprise data work. It helps enterprises execute complex data work with better economics, stronger governance, and greater reliability.

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Why Tera Harness

Own the execution layer

The harness controls how models, tools, and data are used in enterprise AI work, making it the critical layer for managing cost, governance, and long-term AI flexibility.

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Built on open source

Powered by Loom, the open-source agent framework built by Teradata, giving enterprises an execution layer they can inspect, extend, and adapt

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Connected to your environment

Works across models, MCP tools, applications, databases, and vector stores without forcing everything into a closed ecosystem

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Deployed on your terms

Runs across cloud, hybrid, sovereign, and on-premises environments with choice across any model you want to use

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Governed by design

Applies policies, permissions, and human approvals so high-risk actions are intercepted before they occur

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Better AI economics

Minimizes repeated inferencing, reduces token consumption, and routes work to the most efficient execution path

How it works

Plan with context. Execute with control. Finish reliably.

Three layers within Tera, our agentic coworker, drive enterprise data work from intent to governed outcome. Tera Harness coordinates them all—Context Engine, Agent Skills, and the models and tools your enterprise already uses. 

Diagram of Tera Harness continuous cycle of data ingestion, context and knowledge application, and autonomous processing

Here's how the loop runs:

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Understands the request

Brings in the right business context, session history, policies, and task-specific knowledge before execution begins

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Plans the work

Uses proven execution patterns to determine the sequence of steps, relevant skills, tools, models, and approvals

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Executes efficiently

Routes each sub-task to the appropriate engine, batches related operations, and avoids repeatedly processing context the model already knows

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Governs every action

Evaluates high-risk operations before execution and routes restricted actions for human approval

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Maintains state

Checkpoints progress, preserves context, and pauses long-running work while waiting for people or external events

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Recovers and completes

Restores the exact execution state after failures or restarts and continues the workflow without starting over

Purpose-built architecture

An execution engine designed for enterprise data work

Tera Harness has architectural advantages that improve the cost, control, scale, and reliability of agentic execution. 

Cost-optimized loop

Chooses the cheapest reliable engine for each sub-task, preserves cached context, batches reads and writes, and applies execution patterns before inference to cut unnecessary cost.

Loop-embedded guardrails

Intercepts unauthorized writes, destructive SQL, and schema changes before execution. Configures which operations are allowed, denied, or require human approval—built inside the loop, not bolted on.

Go-native performance core

A Go-native runtime and gRPC service layer built for high-concurrency execution with low communication overhead—so the harness itself never becomes the bottleneck.

Durable agentic execution

Runs for days or weeks at near-zero compute cost between steps. State, checkpointing, and automatic recovery are embedded—so work resumes where it left off after any failure.

Measured performance

See the difference Tera Harness makes

Across 4 public benchmarks, Tera delivered better accuracy with materially better economics.

Watch Tera and Claude Code work through the same real-world software engineering task using the same underlying model. Tera completes the work with fewer tokens, lower cost, and less elapsed time. 

SWE-bench Pro

Benchmark measuring real-world software engineering across production codebases. 

  • 73%

    fewer tokens

  • 59%

    lower cost

  • 42%

    faster

  • 1.1%

    higher accuracy

On 731 real software engineering tasks across 11 production codebases, Tera solved more tasks while using substantially fewer tokens and less time, at a lower total cost than Claude Code.

DataAgentBench

Benchmark measuring complex analytics across structured and unstructured data. 

  • 52%

    lower cost per correct answer

  • 30%

    faster

  • 10%

    fewer agent turns

  • 2.5%

    higher accuracy

Tera answered more complex analytical questions correctly while reaching those answers with fewer steps, less time, and approximately half the cost of Claude Code.

Data-Eng-Bench

Benchmark measuring real-world software engineering across production codebases. 

  • 65%

    higher Pass³ score

  • 60%

    fewer tokens

  • 53%

    lower cost per reliably solved task

Tera delivered the benchmark’s highest reliability while cutting the cost of a reliably solved task by more than half compared with Snowflake Cortex Code

ADE-Bench

Benchmark measuring maintaining and evolving real-world dbt analytics projects.

  • 10%

    more tasks resolved

  • 40%

    lower cost

  • 40%

    faster

Tera resolved more real-world dbt development and maintenance tasks while completing the work 40% faster and at 40% lower cost than Claude Code.

Related resources

Frequently asked questions

Tera Harness FAQs

An agent harness is the execution layer around AI models. It determines how context is assembled, how work is planned, which skills, tools, and models are used, how actions are governed, and how state, recovery, and observability are managed. 

Tera Harness is the intelligent execution engine within Tera, Teradata’s agentic coworker for enterprise data work. It coordinates context, planning, memory, skills, tools, models, guardrails, approvals, state, recovery, and observability for complex enterprise data work. 

Tera Harness is purpose-built for enterprise data work. General-purpose frameworks primarily coordinate models and tools, but Tera Harness focuses on reliable execution, not only orchestration. It combines a cost-optimized loop, embedded guardrails, a Go-native performance core, and durable state and recovery. 

Tera Harness reduces repeated model work through cost-aware model selection, cache-aware context, batched tool calls, and proven execution patterns. By reducing turns, tokens, latency, and rework, it helps organizations achieve faster outcomes at a lower cost.

Pre-inference guardrails evaluate high-risk operations before execution. Administrators can define allowed, denied, and approval-required actions and configure model, skill, context, and policy controls. 

Yes. Built-in state, checkpointing, pause-and-resume, and automatic recovery are designed to preserve progress across approvals, external events, restarts, and infrastructure failures.

Tera Harness is designed to support open-weight and frontier models. You can bring whatever model you choose. And you can deploy the Tera Harness across cloud, hybrid, sovereign, and on-premises environments. 

Both are infrastructure layers within Tera, Teradata’s agentic coworker for enterprise data work. Tera Context Engine provides governed business understanding. Tera Harness uses context to plan, coordinate, govern, and execute the work.

Experience Tera firsthand

Request early access to see how Tera can help you complete complex data and AI work faster, more accurately, and with better economics. 



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