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Smriti AI

AI Harnesses

The engineering that turns a model into a dependable system.

Our own engineering

A harness decides what a model sees, what it may do and how its work is checked. We design and build it ourselves, around third-party or Smriti-developed models chosen for the task.

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Illustrative 3D render of an AI harness: a model at the centre, ringed by modules for context, retrieval, tools, rules and evaluation.
Illustrative 3D render

Application and people

Requests from interfaces, voice and other systems

Harness boundary · engineered by Smriti

Model

Replaceable

Third-party or Smriti-developed, chosen per task and changed only after evaluation.

CTXContext handling

What the model sees

RETRetrieval

Approved sources, with permissions

ORCOrchestration

Multi-step execution

TLSTool integration

Defined interfaces to systems

RULWorkflow rules

Boundaries, approvals, fallbacks

EVLEvaluation

Checked before significant changes

DEPDeployment logic

Versioning, rollout, rollback

Approved knowledge, tools and business systems

Reached through retrieval and tool integration

DrawingAn AI harness around a model
TypeSystem diagram
PracticeAI Harnesses

Why the harness matters

Most of what makes an AI system useful sits outside the model.

Two systems built on the same model can behave very differently. The difference is the harness: the context it assembles, the tools and rules it enforces, and the evaluation it is held to.

What it knows
On its ownIts training, plus whatever is pasted into a prompt.
In a harnessApproved sources, retrieved with the user's permissions applied.
What it can do
On its ownProduce a response.
In a harnessTake defined steps in business systems, within rules.
When it is unsure
On its ownNothing defines what should happen.
In a harnessDefined fallbacks, including hand-off to a person.
How it is checked
On its ownSpot checks.
In a harnessTask-specific test sets before every significant change.
Changing the model
On its ownUnclear what else changes.
In a harnessSwapped behind a stable interface, then re-evaluated.

What a harness does

Seven responsibilities, engineered together.

Each one is designed with the others in mind. The codes match the drawing at the top of this page.
CTXContext handling
Assembling the instructions, conversation state and facts each request needs, within the model's limits — and leaving out what it should not see.
RETRetrieval
Finding the right passages in the organisation's approved sources, with each user's permissions applied before anything reaches the model.
ORCOrchestration
Planning multi-step work, handing off between agents, retrying sensibly and handling errors without losing the thread.
TLSTool integration
Connecting to business systems through defined interfaces, with inputs validated before a call and outputs checked after it.
RULWorkflow rules
The boundaries the business sets: what may happen automatically, what needs a person's approval and what to do when the system is unsure.
EVLEvaluation
Task-specific test sets and checks, run before every significant change to a model, prompt, tool or rule.
DEPDeployment logic
Versioned configurations, staged rollout, monitoring in use and a clear path back when something regresses.

Evaluation approach

A harness is only as good as the way it is checked.

Evaluation is built with the harness, not added afterwards. It runs before every release and keeps running once the system is in use.
  1. 01

    Define the task

    What the system must do and what a good result looks like, agreed with the people who own the work.

  2. 02

    Build test sets

    Examples drawn from real work, including the difficult and unusual cases.

  3. 03

    Run checks

    Automated checks where an answer can be verified; human review where judgement is needed.

  4. 04

    Compare

    Models, prompts and configurations measured on the same test set.

  5. 05

    Gate the release

    A change ships only when it holds up on the tasks that matter.

  6. 06

    Monitor in use

    Outcomes reviewed after release. Failures become new test cases.

Findings from use return to step 02, the test set

DrawingEvaluation loop for an AI harness
TypeProcess
PracticeAI Harnesses

  • Measured on the task

    Test sets reflect the organisation's real work, not a generic benchmark.

  • Shortfalls are recorded

    Where the system falls short is documented and shared, not left out.

  • Results come with conditions

    When we report a measurement, we state what was tested and how.

Model-agnostic

The model is a choice, not a commitment.

A harness keeps the interface to the rest of the system stable, so the model behind it can be chosen per task — and changed when evaluation says so.

  • Gemini models

    On Google Cloud, accessed through Gemini Enterprise Agent Platform where a project uses it.

    Google Cloud & Gemini

  • Foundation models available through Amazon Bedrock

    Models from multiple AI companies, compared against the task before one is chosen.

    AWS & Amazon Bedrock

  • Smriti-developed models

    Such as our own voice models, used where they suit the stage they serve.

    Voice AI

Capability overview

AI harnesses and evaluation systems

The engineering around a model that makes it dependable: context handling, orchestration, tool integration, retrieval, workflow rules, evaluation and deployment logic.

A capability overview describes what we engineer in this kind of project. It is not a case study: it names no customers and claims no results.

AI Harnesses

A model that works in a demo but not in the business?

Tell us what it needs to do reliably, and where it falls short today. We will look at the harness first.
Discuss an enterprise AI projectEnterprise AI overview