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Agentic AI Solutions

AI agents that use your tools — under your controls

We design and build AI agents and multi-agent workflows that work through a task, call approved tools and APIs, and hand off to people wherever judgment or authority is required.

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What we cover

  • Enterprise AI agents
  • Multi-agent orchestration
  • Tool calling and MCP integrations
  • Retrieval for grounded decisions
  • Human-in-the-loop approvals
  • Evaluation, observability and audit trails

Outcome: Agents that complete real work within the limits you set — and show their working.

AI Engineering

From answering questions to completing work

A chatbot answers questions. An AI agent can also act: it plans the steps of a task, looks up information, and calls the tools it has been given — creating a ticket, updating a record, drafting a document — then reports what it did.

That ability is useful because it is constrained. The agents we build work inside a defined workflow, with permissions scoped to the task, approval steps where actions carry risk, and logs that show every decision and tool call.

Agents are not the right answer to every problem. Where a fixed workflow or a simpler assistant would do the job more reliably, we'll recommend that instead.

Problems we solve

Sound familiar?

  • Repetitive multi-step work

    Tasks that mean gathering information from several systems, making a routine decision and taking a routine action.

  • Assistants that stop at answers

    Chatbots that explain what to do but can't do any of it, so people still switch between tools.

  • Concern about control

    Uncertainty about what an AI system might do on its own, and how to limit and audit it.

  • One-off integrations

    Every new AI use case needing its own custom connection to internal systems.

Capabilities

What we deliver

We scope each engagement to the problem — not every project needs every capability.

Agent design

Agents scoped to a clear job

Each agent gets a defined goal, a limited set of tools and explicit rules for when to stop or escalate.

  • Task and tool design. Breaking a workflow into steps, deciding which ones an agent should handle, and defining the tools it may call.
  • Tool-calling agents. Agents that call APIs, query databases and use internal services through typed, validated interfaces.
  • Multi-agent orchestration. Coordinating specialized agents — for example research, drafting and review — with frameworks such as LangGraph when one agent is not enough.

Integration

Connected through governed interfaces

Consistent, auditable connections between agents and your enterprise systems.

  • Model Context Protocol (MCP). MCP servers that expose your tools and data to agents through one consistent, permissioned interface.
  • API and application integration. Connections to ITSM, CRM, ERP, document and messaging systems using your existing authentication.
  • Retrieval-augmented generation. Grounding agent decisions in your policies, knowledge base and records, with sources a reviewer can check.

Controls

Guardrails that match the risk of each action

The more consequential the action, the tighter the control.

  • Human-in-the-loop approvals. Review and approval steps before actions such as sending, paying, deleting or changing access.
  • Authorization. Agents act with scoped credentials and the requesting user's permissions — never broader access than the task needs.
  • Guardrails and audit trails. Input and output checks, limits on actions, and a complete log of prompts, tool calls and results.

Operations

Evaluated before launch, observed after

Agents are tested like software and monitored like production systems.

  • Agent evaluation. Scenario-based test suites that measure task success, tool-use accuracy and failure handling before each release.
  • Observability. Tracing of every step, with dashboards for quality, latency, cost and escalation rates.
  • Deployment and lifecycle management. Versioned prompts, tools and models, released gradually and with a rollback path.

Use cases

Where it makes a difference

Typical scenarios, to show the kind of work involved.

  • IT operations

    Triage incoming tickets, gather diagnostics, suggest or run approved remediation steps, and escalate with full context.

  • Customer support

    Resolve routine requests end to end and hand complex cases to a person with a summary of what has been checked.

  • Document processing

    Read, classify and extract data from documents, check it against business rules, and route exceptions for review.

  • Knowledge assistants

    Answer staff questions from policies and internal knowledge, and take simple follow-up actions such as filing a request.

  • Business process automation

    Coordinate multi-step processes such as onboarding or procurement across several systems, with approvals at key points.

How we deliver

From first conversation to production

  1. Select the use case

    Choose a workflow where an agent adds value and the risks can be controlled.

  2. Design

    Define the steps, tools, permissions, approval points and success measures.

  3. Build & integrate

    Implement the agent and its tools, and connect them to your systems.

  4. Evaluate

    Run scenario tests, including edge cases and attempts to misuse the agent.

  5. Pilot

    Release to a small group of users with close monitoring and easy escalation.

  6. Scale & operate

    Widen access, monitor quality and cost, and improve from real usage.

Technology

Tools we work with

Chosen to fit your environment and team, not a preferred vendor.

Agent frameworks

  • LangGraph
  • LangChain
  • Model Context Protocol (MCP)

Models

  • OpenAI
  • Anthropic Claude
  • Azure OpenAI
  • AWS Bedrock
  • Open-source models

Integration

  • REST and GraphQL APIs
  • Webhooks
  • Message queues
  • OAuth and OpenID Connect

Operations

  • OpenTelemetry
  • Evaluation test suites
  • Docker
  • Kubernetes

Built for production

Security, scale and operations

  • Least privilege

    Each tool exposes only the operations the task needs, and agents act with the requesting user's permissions.

  • Data privacy

    Model providers and hosting are chosen to meet your data requirements, including private endpoints in your own cloud tenant.

  • Reliability

    Deterministic code handles what doesn't need judgment; agents retry safely, time out and escalate when they are unsure.

  • Auditability

    Every prompt, tool call, approval and result is logged, so any outcome can be traced and explained.

Engagement options

Work with us the way that fits

We'll recommend a model after the first conversation.

  • Fixed-price

    A defined scope and budget for a clear deliverable — ideal for builds, migrations and assessments.

  • Time & materials

    Flexible capacity, invoiced monthly, for work where the scope will evolve as you learn.

  • Dedicated team

    A full-time pod embedded with your team for long-running products and platforms.

  • Outcome-based

    Fees tied to the KPIs we agree up front, where the result can be measured cleanly.

FAQ

Common questions

Something else? Ask our team

What's the difference between a chatbot and an AI agent?

A chatbot generates answers. An agent can also take actions by calling tools it has been given — such as searching a system, creating a ticket or updating a record — and works through several steps to complete a task. The agents we build act only within defined permissions and approval rules.

How do you stop an agent from doing something it shouldn't?

By design rather than by hope: each agent can only use the tools and permissions its task requires, consequential actions need human approval, inputs and outputs are checked, and every action is logged for review.

What is MCP, and do we need it?

The Model Context Protocol is an open standard for connecting AI applications to tools and data. It is useful when several agents or AI applications need the same governed access to your systems. For a single, narrow integration a direct API connection can be simpler.

Can the agents run in our own cloud?

Yes. Agents and their tools can be deployed in your AWS, Azure or Google Cloud account, using model endpoints that meet your data-residency and privacy requirements.

How do you know an agent is working?

Before launch, agents are tested against scenario suites with known correct outcomes. In production we track task success, escalation rate, user feedback, latency and cost, and add any failures to the test suite.

Resources

Related reading

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Related services

Have a workflow an agent could take on?

Walk us through the process, the systems involved and where people need to stay in control. We'll tell you honestly whether an agent is the right fit.

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