Agentic AI & Automation

Agentic AI Consulting

Agents change what a process can look like, not just how fast it runs. That makes it an operating design question before it is an engineering one: which decisions stay with people, what an agent is permitted to do alone, and who answers for the outcome.

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The business problem

Automating a broken process makes it broken faster

Most processes have accumulated steps that exist because of a system limitation, an old audit finding, or a person who once made a mistake. Automating that faithfully preserves all of it. The organizations getting real value from agents are redesigning the process around what agents can do, which raises questions about accountability and control that no engineering team can answer alone.

What we do

Design the process and the control model together

We map how the work is done now and where the judgement genuinely sits, then design a target process that assigns work between people and agents deliberately. That comes with an explicit control model: what an agent may do unsupervised, what requires approval, what is logged, and who is accountable when it goes wrong. We also define how the organization will know it is working, because an agent that quietly degrades is worse than one that visibly fails.

Agentic AI Consulting

Capabilities

Agent Strategy
Agentic Workflow Design
Business Process Redesign
Human-in-the-Loop Architecture
Multi-Agent Architecture
Agent Governance
Agent Operating Models

Common use cases

Redesign a back-office process around agents rather than automating its current shape.
Establish an organizational position on what agents may do without human approval.
Assess a proposed multi-agent architecture before it becomes expensive to change.
Define accountability for agent decisions in a regulated process.

How we deliver

Process discovery

Map the current path, its branches, its exceptions and its definition of correct.
Bounded prototype

One process, read-only first, measured against a labelled set before it writes anything.
Guardrails & approval

Permissions, rate limits, approval gates and audit logging before production access.
Production & operate

Staged rollout with continuous evaluation, cost monitoring and a rollback path.

Technology

OpenAI
Anthropic
Google Vertex AI
Azure OpenAI
AWS Bedrock
Model Context Protocol
LangGraph
Temporal
OpenTelemetry

Security & governance

Software that acts inside your systems is a privileged identity and is treated as one. It authenticates through your identity provider, holds least-privilege scopes, and cannot exceed the permissions of the user on whose behalf it acts. Tool calls are validated and rate-limited, untrusted content is treated as data rather than instruction to limit prompt injection, and consequential actions require human approval. Every run is logged with inputs, calls and outputs, which is what makes the system auditable.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Forward-Deployed AI Team

A cross-functional AI team works inside your organization to continuously find and deliver opportunities.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We have had to make these choices in production

The questions that decide whether an agent programme works are practical: how much autonomy, what to log, when to stop. Answering them well requires having lived with the consequences, which is different from having read about them. We build and operate these systems, so the advice is grounded in what actually failed.

Selected clients

Frequently asked questions

How much autonomy should an agent have?

Start with none and earn it. Read-only first, then write access on reversible actions, then approval-gated actions, with each step justified by measured accuracy. Autonomy granted upfront tends to be withdrawn after the first incident, which costs more than starting narrow.

Do we need multiple agents?

Usually fewer than proposed. Multi-agent architectures add coordination failure modes, and most processes that appear to need several agents actually need one agent with several tools. We recommend the simpler structure unless the work genuinely decomposes.

Who is accountable when an agent gets it wrong?

A named person, always. Part of this work is deciding who that is for each process and making sure they have the visibility to exercise the responsibility. An accountability model that stops at 'the system' does not survive an incident.

How does this affect our people?

Honestly, it changes roles, and pretending otherwise makes adoption harder. We work through which tasks move, what the remaining human work becomes, and what that means for the shape of the team, as part of the design rather than after it.

Related capabilities

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Decide where AI belongs in your business, what it is worth, and in what order to build it.

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AI Infrastructure & LLMOps

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Design the reference architecture your AI systems share: models, retrieval, orchestration, data and controls.

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AI Security

AI Security Consulting

Threat-model AI systems and design the architecture, controls and boundaries that contain their risk.

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Discuss Your AI Initiative

Redesign business processes around agents that reason, coordinate tools and complete multi-step work.

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