Agentic AI Consulting & Implementation

Agentic AI consulting that reaches production.

InTheCloud provides agentic AI consulting and implementation for enterprises: agent architecture, MCP and API integrations, tool calling and orchestration, retrieval, human-in-the-loop controls, evaluation and observability — shipped by senior engineers embedded with your team.

We design and build production agentic AI systems that pass enterprise security, compliance and reliability bars. Engagements start with one workflow and a measurable number, reach an evaluated pilot on your own data in 4 to 8 weeks, and land in production with cost controls, audit logging and approval gates in place — owned by your engineers afterwards.

What we build: Plan-act-observe agent loops with step budgets, tool allowlists, retries and approval gates — wired into systems of record rather than into a sandbox.
Integration layer: MCP servers and service APIs with identity pass-through, least-privilege scopes, idempotent writes and full tool-call logging.
Quality control: Golden datasets, rubric graders, adversarial cases in CI, drift alerts on live traffic, and versioned prompt and model records.
Typical timeline: 4 to 8 weeks to an evaluated pilot on your own data; 3 to 6 months to a production agent through security review.

How we deliver agentic AI

Use-case shaping

We pick the workflow where multi-step autonomy pays — repetitive, high-volume, checkable — and attach the number the engagement will be judged on.

Agent architecture

We design the loop, the memory, the tool surface and the failure behaviour before writing prompts, so the system is debuggable when it misbehaves.

Evaluated pilot

We ship a working agent against your data with an evaluation harness from day one, so quality is measured rather than demonstrated.

Production and governance

We harden the system — observability, cost controls, audit logging, approvals — and hand it to your engineers with runbooks and eval suites they own.

What we build

Enterprise agent architecture

A bounded plan-act-observe loop with explicit step budgets, a typed tool surface, structured intermediate state and deterministic fallbacks — so behaviour is inspectable rather than emergent.

MCP and enterprise integrations

An MCP server exposes narrow, well-described tools over existing APIs, with identity pass-through so the agent acts as the user, least-privilege scopes, and idempotency keys on every write.

Tool calling and workflow orchestration

Orchestration that separates planning from execution, retries transient failures, short-circuits on policy violations, and records each call so a run can be replayed end to end.

Retrieval over enterprise data

Hybrid keyword and embedding retrieval with access control applied at query time and citation on every answer, tuned on your own queries rather than a public benchmark.

Human-in-the-loop controls

Approval surfaces on anything irreversible or regulated, with the agent's evidence and proposed action presented for a decision — and the decision written into the audit record.

Evaluation and observability

Golden datasets and rubric graders in CI, prompt and tool-call tracing, cost and latency budgets per run, and drift alerts on live traffic.

Engineering guides from our agentic work

What an enterprise agentic AI pilot actually costs

Where the money goes in a first pilot, what drives the range, and why a second pilot costs materially less than the first.

Read: What an enterprise agentic AI pilot actually costs

Building a production MCP server

Tool design, identity pass-through, idempotent writes and adversarial testing — the integration work that decides whether an agent is safe against systems of record.

Read: Building a production MCP server

An evaluation harness for regulated environments

Golden datasets, rubric grading calibrated against human reviewers, CI release gates and drift monitoring — the evidence a risk function asks for.

Read: An evaluation harness for regulated environments

Bring us the AI initiative you're trying to get into production.

Book a Call with a Builder

What happens next

30-minute builder-led call
No generic sales pitch
Architecture, feasibility and constraints
A recommended next step

Where engagements start: Enterprise AI Proof of Value

A fixed-scope 4–8 week engagement focused on one prioritized use case, producing a working implementation with real enterprise data where appropriate and an evidence-backed decision about production.

— A prioritized use case with a success metric agreed up front
— A target architecture and integration plan for your environment
— A working implementation against your own data
— Model selection and evaluation results, not vendor preference
— A governance and security review with your own reviewers
— A production roadmap and an honest go/no-go recommendation

Discuss an AI Proof of Value

What does it take to move agentic AI into production?

Bounding it. Production agents have step budgets, tool allowlists, timeouts, idempotent writes and a defined behaviour on every failure path. Without those, a demo that works nine times in ten becomes an incident at volume.

Then evidence. An evaluation suite in the pipeline, thresholds that gate releases, tracing on prompts and tool calls, and cost and latency budgets enforced per run. That is what turns weekly prompt changes from a risk into routine engineering.

Finally ownership. We hand over runbooks, eval suites and the integration layer to your engineers, because an agent nobody owns degrades quietly the moment the underlying systems change.

How do you prove value before committing to a program?

With a fixed-scope proof of value over 4 to 8 weeks against real data, ending in a working implementation, evaluation results, a security and governance review, and an honest go/no-go recommendation.

That structure caps your exposure and produces evidence that improves the rest of the roadmap — including the cases where the answer is that a simpler non-agentic service does the job.

What security and governance does agentic AI require?

Containment first: least-privilege tool scopes, identity pass-through so the agent inherits the user's permissions, no egress to public model endpoints where policy forbids it, and PII handling agreed with your privacy function.

Then accountability: for any action you should be able to show the inputs, retrieved sources, tool calls, model and prompt version, and the human approval that followed — and reproduce it on demand.

Frequently asked questions

What is agentic AI consulting?

Agentic AI consulting is the work of choosing, designing and building systems where a model plans and executes multi-step tasks by calling tools, retrieving context and acting on enterprise systems. Done properly it covers agent architecture, integrations, evaluation, observability and governance — not prompt writing.

What does InTheCloud deliver in an agentic AI engagement?

A senior pod embedded with your team, shipping production agentic AI: agent architecture, MCP and API integrations, retrieval pipelines, evaluation harnesses, observability, guardrails and the surrounding software engineering — built to enterprise security, compliance and reliability bars.

Which foundation models and frameworks do you use?

We are model- and framework-agnostic. We work with Anthropic Claude, OpenAI GPT, Google Gemini and open-weight models on AWS, Azure and Google Cloud. Framework choice — LangGraph, the Vercel AI SDK, custom orchestration — follows the workload and your existing stack.

How do you handle evaluation, safety and governance?

Every agentic system we ship includes evaluation suites, regression harnesses, prompt and tool-call observability, cost tracking and policy guardrails. For regulated workloads we layer in audit logging, PII handling and human-in-the-loop checkpoints aligned to your AI risk framework.

How much does an agentic AI pilot cost?

A fixed-scope proof of value typically runs 4 to 8 weeks with a small senior team, and the cost is driven by integration surface and compliance scope far more than by the model. Our published breakdown of what a first enterprise pilot costs sets out where the money actually goes.

How do agents integrate with our existing systems?

Through the APIs, event streams and databases you already run, exposed to the model as narrow, well-described tools — often via an MCP server — with identity pass-through, least-privilege scopes and idempotent writes so a retry cannot double-apply an action.

When is agentic AI the wrong answer?

When the task is a single deterministic step, when there is no measurable outcome attached, or when nobody will own the workflow afterwards. In those cases a straightforward retrieval or classification service is cheaper, faster and easier to defend.