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AI automation & agents

Agentic AI Development

Give an agent a bounded job. Keep its authority clear.

Agentic AI uses model-driven planning or tool selection to work towards a defined objective. A business agent needs permitted tools, relevant knowledge, action limits and a reliable handoff when it cannot complete the task safely.

REIT Limited · Bangladesh-based AI services and technical-delivery company

See the work in context

A practical scenario.
A decision you can inspect.

Explore the inputs, human controls and trade-offs behind a proposed use case.

Download example data (CSV)
Fictional case study · Meridian Help StudioSynthetic data · not a REIT Limited client result
support requests / illustrative scenario

Support answers with a source behind them

A fictional software-support team works from approved product articles. Reviewers search several documents before preparing each answer. Unsupported questions need a clear escalation path.

Proposed workflow
Read the request · Retrieve approved sources · Prepare a cited draft · Review and respond
Human control
A support reviewer checks the sources and approves the final answer.

Human effort per month

Hours · review included
0180 hours
69 hmodelled capacity change / month

38.3% less human effort under these assumptions. This is not cash savings.

Synthetic incoming workload mixKnown topics: 540 (60%); Context-dependent: 270 (30%); Escalation cases: 90 (10%). Total 900 support requests.900ITEMS / MONTH

Workload mix

  • Known topics60%
  • Context-dependent30%
  • Escalation cases10%

Assumed distribution, not measured activity.

Inspect the data, calculation and pilot decision
Meridian Help Studio: fictional assumptions for a representative month
Input or calculated measureIllustrative value
Monthly in-scope volume900 support requests
Before: human minutes per item12
Proposed: human minutes per item7
Additional operating hours per month6
Baseline human hours per month180
Proposed human hours per month111
Net capacity change per month69 hours
Illustrative pilot window5 weeks; not a delivery commitment
Assumed mix: Known topics540 items
Assumed mix: Context-dependent270 items
Assumed mix: Escalation cases90 items

Calculation: 900 × 12 ÷ 60 = 180 baseline hours. Proposed: 900 × 7 ÷ 60 + 6 operating hours = 111 hours.

Pilot decision: Do sources support the answer, and are correction effort and reopened requests included?

Limit: Faster drafts are not a benefit if answer quality deteriorates. No autonomous customer reply is assumed.

Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.

The business problem

Give an agent a bounded job. Keep its authority clear.

Some requests need interpretation before the next step is clear. A support assistant may need to find the right policy, inspect permitted context and prepare an answer. REIT Limited designs that task boundary before giving an agent access to business systems.

Where it fits

Useful starting points.

Knowledge-assisted support

Retrieve approved knowledge and prepare source-linked answers for a support reviewer. Define the knowledge boundary, ticket context and situations requiring escalation.

Bounded research and reporting

Gather information from permitted sources and produce a structured draft with references. Specify the question, acceptable sources and the checks a reviewer will apply.

Operational assistance

Choose among approved tools to classify a request or prepare a system update. Define allowed reads and writes, spending limits and the final approval authority.

Scope & deliverables

What a scoped engagement can include.

A task and permission contract

A written objective, allowed tools, knowledge sources, action limits, refusal conditions and human handoff. Credentials and production access remain subject to agreed controls.

An evaluated agent

Task planning, contextual retrieval and controlled tool use tested against representative cases. Evaluation includes incomplete evidence, malicious instructions in retrieved content and tool failure.

Traceable operation

Action traces, failure handling, usage controls and an operating guide. Document who reviews exceptions, changes prompts and approves releases.

What we need from you

Provide the task, permitted information sources, example questions and tool/API documentation. Identify the reviewer and the actions an agent must never perform without approval.

Where the boundary sits

An agent is not a substitute for clear process ownership or specialist judgement. Broad production permissions, unrestricted spending and open-ended autonomous operation are not assumed.

Process & control

Know what happens next.

From input to an accountable outcomeIllustrative workflow
  1. Bounded request
  2. Retrieve and plan
  3. Approve action
  4. Execute and log

Illustrative example: a support agent retrieves a product policy, drafts a referenced answer and asks a reviewer to approve it. If the source is missing or conflicting, it escalates rather than presenting an unsupported answer.

Quality & operation

Agree the evidence before the build.

Agentic AI Development — proposed acceptance checks
CheckWhat the evidence should show
Task qualityDoes the output satisfy the agreed task and cite the required evidence?
Tool boundariesCan the agent be prevented from using an unapproved action?
HandoffDoes uncertainty or a failed tool call reach the right person?

Acceptance measures, access boundaries, exception handling and operating responsibility belong in the scope. The assessment should also separate implementation work from vendor usage, licences, hosting, support and approved change requests.

Buyer questions

A few details that matter.

When is a workflow the better choice?

A defined workflow is often simpler when the decisions can be expressed as stable rules. Workflows can branch and call tools; an agent is useful when bounded interpretation or planning is genuinely needed.

Can an agent connect to our CRM?

Possibly, subject to API availability, access permissions, data quality and vendor limits. The assessment checks the connection and defines exactly what the agent may read or change.

Can we prevent every incorrect answer?

No system should be described as error-free. Use relevant evaluations, source checks, permission boundaries and human review to identify and manage failure within the agreed use case.

Start with one useful change

What would you like your business to do better?

Tell us about the process, the systems and the result you need. We can discuss a sensible first scope.

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