The programmes people are actually running.
- patterns, each with its evidence basis stated on the page
- 13
- written from work we have delivered
- 5
- method we would bring, said so up front
- 8
- carry a sources list you can open and check
- 13
Two labels, and we do not blur them
Most firms publish implementation guides for everything and let you assume delivery behind all of it. These are split by what is actually behind them.
Backed by work we have actually done, on real engagements. The guide is written from that work rather than from vendor documentation, and it says plainly where the delivery stopped.
Jump to theseWe have not run this at enterprise scale. The guide is the method we would bring, and it names exactly what we are drawing on instead, at the top of the page rather than in a footnote.
Jump to theseCommon in regulated financial servicesmarks the three UK insurers and banks ask us about most. What each regime requires of an agent is written up on agentic transformation in financial services.
If the distinction between those two labels matters to your procurement, we will walk it through on the call.
Talk it throughIf you arrived with a sentence rather than a category
Each of these is answered in full inside one of the guides, usually in the questions section at the foot of it.
- What is a target operating model for an AI-native organisation?
- Do we restructure before deploying agents, or after?
- How do we stop our AI agent hallucinating?
- Do we need to fine-tune, or use RAG?
- What is the ROI of an agentic AI programme?
- How do we stop retrieval leaking documents to the wrong people?
- Should we adopt MCP, or is function calling enough?
- What does an evaluation harness actually consist of?
- What does it cost to run, not to build?
- How do we stop an agent taking an action it cannot undo?
- Does a bigger context window remove the need for RAG?
- Our pilot worked and never shipped. What now?
- When do the EU AI Act high-risk obligations actually apply?
- Should an agent have its own identity?
- We are drowning in PDFs. Where do we start?
- Can we use our existing call recordings?
- Why has AI assistance not moved our cycle times?
- Why has AI coding tool adoption stalled here?
- What actually breaks after week two?
- Who is accountable when an agent gets it wrong?
Or bring the sentence to a call and skip the reading.
Talk it throughPatterns we have shipped
Each of these is written from an engagement rather than from research, and each one states where our experience runs out.
Document intelligence to business intelligence
Getting information out of PDFs and into something the business can decide with.
You are here if the extraction demo impressed everyone and the business still will not act on the numbers.
Read the guide14 min readAI-native SDLC and product delivery lifecycle
Changing how software gets specified, built and shipped once AI is in the room.
You are here if the licences are bought, adoption has plateaued, and the delivery lifecycle has not changed.
Read the guide14 min readTarget operating model for an AI-native organisation
What changes in structure, roles and decision rights once agents do a share of the work.
You are here if the pilot worked and scaling it now needs roles, decision rights and governance to move.
Read the guide15 min readVoice agents and conversation intelligence
Turning conversations into structured intelligence, and holding conversations that take real actions.
You are here if the call recordings exist and nothing structured is coming out of them.
Read the guide13 min readEnd-to-end agentic workflow implementation
Taking one whole business process agentic, rather than assisting the humans doing it.
You are here if every step of a process now has an assistant and the process itself is unchanged.
Read the guide17 min readIf one of these is the pattern you need, ask what it looked like in delivery.
Talk it throughPatterns we would be approaching for the first time
We have not stood these up at enterprise scale. What we publish is the method, the failure modes we would design against, and what we are drawing on instead.
How it is built
The engineering underneath an agent: retrieval, tools, accuracy control, and choosing between them.
Retrieval, RAG and permission-aware knowledge access
Answering from your own knowledge, without answering from documents the person asking is not allowed to see.
You are here if the assistant answers well and nobody can prove it only answers from what the asker may see.
Read the guide17 min readMCP, tool calling and integrating agents with your systems
The moment an agent can call your systems, integration stops being plumbing and becomes an access decision.
You are here if the agent can now call your systems and nobody decided whose authority the call carries.
Read the guide26 min readGuardrails, hallucination and accuracy control
You will not stop a language model being wrong. You can decide in advance what happens when it is.
You are here if the answer to how you stop it being wrong is currently a paragraph in the system prompt.
Read the guide17 min readRAG, fine-tuning or prompting: how to choose
Sorting the requirement into knowledge, behaviour and cost, so the decision survives a finance review.
You are here if the architecture argument is being won by whoever read the most recent article.
Read the guide16 min readProving it and controlling it
Evidence, identity and governance. The parts that decide whether anything reaches production.
Agent evaluation and assurance
Why AI pilots never reach production, and what it takes to get one through the gate.
You are here if the pilot was judged by demonstration, and nobody will sign it off for production.
Read the guide15 min readAI governance and regulatory evidence
Building the evidence as a by-product of the work, rather than assembling it under deadline.
You are here if the evidence pack is being assembled by hand, after the fact, against a deadline.
Read the guide13 min readAgent identity and access
Agents are not users, and giving them a service account is how this goes wrong.
You are here if an agent is about to be given a service account and nobody can say what it is allowed to do.
Read the guide13 min readPaying for it
The money, the payback arithmetic, and what a finance function should refuse to accept.
If one of these is the thing you need bought rather than reasoned about, say so on the call and we will tell you whether we are the right firm for it.
If you need one of these done rather than described, the call is where we say whether we would be doing it for the first time.
Talk it throughDescribe the problem and we will point you at the guide
Answers come from the 13 guides themselves, and the assistant will tell you first whether the pattern you are asking about is one we have delivered.
Prefer to talk it through? Ask us on a discovery call →
Or describe it on a call and we will point you at the guide, or tell you there is not one yet.
Talk it throughRunning one of these?
Thirty minutes with James Rooney. We will tell you honestly which parts of it we have done before and which we would be doing for the first time.
most start with a fixed-price AI Readiness Audit · £44,000 · 4 weeks · working prototypes
Calendar not loading? Open it on cal.com or email hello@tenhaw.com.
Using the guides: your questions
What do Tenhaw's agentic AI guides actually cover?
Thirteen guides to the agentic programmes enterprises are running now, grouped by subject. Whole programmes covers document intelligence, voice agents and conversation intelligence, end-to-end agentic workflows, the AI-native SDLC and the target operating model for an AI-native organisation. How it is built covers retrieval and RAG, MCP and tool calling, guardrails and accuracy control, and choosing between RAG, fine-tuning and prompting. Proving it and controlling it covers agent evaluation, governance and regulatory evidence, and agent identity and access. Paying for it is the business case: ROI, payback and what a finance function should refuse to accept. Every guide sets out why the pattern stalls, how Tenhaw would sequence it, and whether it is written from delivered work.
How do I know which guide to start with?
Start from the symptom rather than the title. Every card on Tenhaw's guides hub carries a line drawn from that guide's own failure modes: if the pilot was judged by demonstration and nobody will sign it off for production, that is agent evaluation and assurance; if you are drowning in PDFs the business will not act on, document intelligence; if the AI coding licences are bought and adoption has plateaued, the AI-native SDLC; if the benefit is stated in hours and finance has started asking which budget line moves, the business case. The hub also carries a question index, and an assistant you can describe the problem to.
What do the Delivered and Our approach labels on the guides mean?
Delivered means the guide is written from work Tenhaw has actually done on real engagements, and it says plainly where the delivery stopped, so a proof of concept is labelled a proof of concept, never production. Our approach means Tenhaw has not run that pattern at enterprise scale, and the guide is the method it would bring, with what it draws on instead named at the top of the page rather than in a footnote. Five of the thirteen guides carry the first label and eight the second, and the split is the structure of the hub rather than small print in a card corner.
Which agentic AI patterns has Tenhaw actually delivered?
Five guides are written from delivered work: document intelligence, the AI-native SDLC, the target operating model, voice agents and conversation intelligence, and end-to-end agentic workflows. Each says where the delivery stopped. The document pipeline ran end to end on Azure on a live insurance engagement, as a working proof of concept rather than a production system. The build method produced a working proof of concept in two weeks, pair-programmed with the client's own engineer. The operating-model discipline was co-designed, piloted and proved at 500-team scope inside HSBC, and that work was not an AI programme. The other eight guides are the method Tenhaw would bring, and say so above the fold.
Which guides matter most to a regulated financial services firm?
Three are marked on the hub as the ones UK insurers and banks ask about most: AI governance and regulatory evidence, document intelligence to business intelligence, and agent evaluation and assurance. Tenhaw's own document-intelligence work ran end to end on Azure on a live London insurance engagement, a working proof of concept in two weeks rather than a production system. Between them they cover the inventory and evidence a regulator will ask for, the document-heavy work where an organisation usually meets agentic AI first, and the release gate that decides whether a pilot ever reaches production. What each regime actually requires of an agent, SM&CR, SS1/23, Consumer Duty, Solvency II and operational resilience, is written up separately on Tenhaw's financial services sector page.
Do the guides cite sources you can check?
Yes. All thirteen of Tenhaw's guides carry a sources list you can open, and the rule applied throughout is that every source was opened and read before it was attached; where nothing survived that check, the claim was softened rather than propped up with a plausible-looking link. So survey figures cite the published survey, research claims cite the paper, and regulatory statements point at the regulator's or legislator's own text, each with a note saying what it supports. It is the same discipline Tenhaw's pricing page runs on, where every competitor day rate is quoted from the supplier's own published rate card and linked to it.
Do the guides name actual tools and systems, or stay vendor-neutral?
They name them, and each name carries the same honesty label the guides do. Where a guide takes a position on a named technology, the item is marked delivered where Tenhaw has shipped on it and our approach where it is a view without a delivery behind it, because naming a technology reads as a claim to have shipped on it. The MCP and tool calling guide goes further and tables six enterprise systems, Salesforce, ServiceNow, SharePoint and Microsoft 365, Snowflake, Databricks and Workday, with each one's authentication and permission model read from the vendor's own documentation on a stated date rather than recalled.
Are the guides just marketing for Tenhaw's services?
They sell by being useful rather than by withholding. Each of Tenhaw's thirteen guides publishes the failure modes, the method and the sources in full, and several end by saying the cheapest first step is work that does not need Tenhaw at all: writing your own engineering standard, running a prompting baseline before anyone builds anything, or spending a fortnight on your corpus and its permission model. Publishing the method is deliberate; The Tenhaw Way and the seventeen how-to guides under it are free to adopt without hiring anyone. Each guide does name the engagement it maps to, with the published price, for the reader who wants the work done rather than described.
What is the next step if a guide describes our problem?
Every Tenhaw guide names the engagement rung it maps to. Guides about proving a pattern in a live workflow point at the Agentic Proof of Concept, £20k–£55k fixed over 2–4 weeks. Guides about designing the operating model, governance and controls point at the Agentic Design Team at £35k–£55k a month. Taking a whole process agentic points at the Agentic Build Team at £70k–£85k a month. The business case guide points at the AI Readiness Audit, £44,000 fixed over four weeks, because where AI delivers value across the business is the question the audit answers first. The lighter next step is thirty minutes with James Rooney, Tenhaw's partner.
What if none of the guides covers our problem?
Bring the sentence to a call. The thirteen guides are the patterns Tenhaw keeps meeting, and the hub says plainly that describing a problem and being told there is not a guide for it yet is a real answer rather than a fudge. Thirty minutes with James Rooney gets you either the guide that covers it or that answer. Equally, if what you need is one of these stood up rather than reasoned about, say so, and Tenhaw will tell you whether it is the right firm for it, including when it would be doing it for the first time.
When is Tenhaw the right firm for a first-of-its-kind build?
When the pattern is new to your estate and the delivery method is not. Everything Tenhaw builds to is published, from the operating model to a 72-rule open-source engineering standard with RFC 2119 severities. The proof points are specific. A London specialty insurance proof of concept took PDFs to business intelligence on Azure in two weeks, pair-programmed with the client's own engineer, who ended the fortnight 70% confident of running the process unaided. Velocity84, Tenhaw's build lab, has produced more than twenty agentic products across voice, video, document reading, mobile and go-to-market. It opens at an Agentic Proof of Concept, £20,000–£55,000 fixed over 2–4 weeks, a stage you can stop, and Tenhaw will say if it is the wrong firm for the work.
Do I have to read a whole guide to get the answer?
No. Tenhaw's guides hub carries an index of twenty questions, the ones technical and finance buyers actually type about RAG, MCP, hallucination, fine-tuning and payback, each linking straight to the guide that answers it, usually in the questions section at the foot of that guide. Every card also shows a read time counted from that guide's own text, so you know what you are committing to before you click. They run from eight minutes to twenty-two, and MCP and tool calling is the longest by some way. The hub and the guide page count that time the same way, so the two never disagree.
Can we use these guides to pressure-test a plan we have been sold?
Yes, and it is a good use of them. Every one of Tenhaw's thirteen guides publishes its failure modes in full, so take the section on why that pattern stalls into the room and ask which of them the proposal has actually designed against. The ones that catch most plans are unglamorous: whose authority an agent's tool calls carry, whether retrieval can return a document the person asking is not allowed to open, and what the thing costs to run rather than to build. Then apply to your supplier the rule Tenhaw applies to itself. Ask which of these patterns they have shipped, and where the delivery stopped.
Are the guides up to date, or written once and left?
They carry dates rather than a claim to be evergreen. Every authentication and permission statement in the MCP and tool calling guide was read from the vendor's own documentation on 26 July 2026, and where a position depends on something a vendor or a regulator can change, a Tenhaw guide says when it was read and tells you to check the source before you design against it rather than after. So the moving parts are named as moving. Salesforce restricting new connected apps from Spring '26, Snowflake blocking password authentication for legacy service users between August and October 2026, and the June 2026 Digital Omnibus vote that pushed stand-alone high-risk EU AI Act obligations out to December 2027.
Are the guides any use before we have run a pilot?
Some of them, and it is worth saying which. Most of Tenhaw's thirteen guides are written for an organisation already past a demonstration, where the pilot worked and scaling it now needs decision rights to move, or the coding licences are bought and adoption has plateaued. Two are useful before anything is built. The business case guide builds the case for approval rather than defending it afterwards, and RAG, fine-tuning or prompting settles the architecture argument, starting with a prompting baseline that costs a few days and needs nobody hired. The same holds for the engineering standard Tenhaw builds to, an open-source handbook of 72 rules with stable identifiers and RFC 2119 severities, published on GitHub and adoptable long before a pilot exists.