Agent Readiness Assessment
Stop guessing where AI agents will actually land in your engineering org.
A fixed-price, three-week diagnostic that maps how your team ships, scores you across eight readiness dimensions, and hands you a prioritised, costed roadmap for adopting AI agents safely — benchmarked against recognised standards, not a homemade maturity score.
Most AI-readiness reports die in week three
A generic maturity score tells you you're a “3.2 out of 5.” Nobody knows what to do with that number on Monday morning. The dominant failure mode in 2026 isn't model quality — it's governance, data and adoption: the unglamorous foundations that decide whether an agent makes it past the demo.
The Agent Readiness Assessment is built to be different. We rank the specific interventions worth doing in your team — by impact, by effort, and by how adoptable they are given how you work today — and we tie every recommendation back to a recognised standard so it reads as benchmarked, not invented.
Eight dimensions, one map
Each dimension is scored 1–5 for a total out of 40, then mapped to one of four readiness bands: Low (8–16), Moderate (17–24), High (25–32) or Agent-native (33–40). Every score carries a confidence rating based on whether it's measured from your systems, corroborated across sources, or a single-session estimate.
Delivery Maturity
CI/CD, test coverage, deployment frequency and rollback capability — can your pipeline safely absorb agent-generated change?
Codebase Readiness
Documentation, modularity, context accessibility and dependency clarity — how legible is your code to an agent (and a new engineer)?
Team Structure
Cognitive-load distribution, knowledge-concentration risk and review bottlenecks that agents could relieve — or amplify.
Agent Infrastructure
Tooling, guardrail capability, verification pipelines and feedback loops — the substrate agents need to act safely.
Change Readiness
Prior adoption track record, champion availability, team sentiment and leadership sponsorship — will the change actually stick?
Revenue & Growth Readiness
Customer metrics, growth experiments and product-market signal — so interventions are ranked on business value, not novelty.
Data & Governance Readiness
Data quality, decision rights, audit trails and metadata standards — the most commonly cited blocker to agent adoption.
Competitive Strategy Clarity
Positioning, defensible capabilities and where an agent advantage is durable rather than quickly copied.
Mapped to recognised standards
Anchoring the assessment to established frameworks is what separates a £14,500 decision-grade deliverable from a slide deck. No single standard is sufficient on its own; together they form a cross-jurisdiction architecture your risk, security and audit functions already recognise.
NIST AI Risk Management Framework (AI RMF 1.0) + Generative AI Profile
We structure your risk register around the framework's Govern–Map–Measure–Manage functions, so governance gaps are named in language your risk and audit functions already recognise.
EU AI Act
We classify each candidate agent use case against the Act's risk tiers (prohibited, high-risk, limited, minimal) so you understand your obligations before you build. Enforcement of the Act's main provisions begins in August 2026.
ISO/IEC 42001:2023 (AI Management System)
We assess your AI governance against the first international AI management-system standard, which shares its structure with ISO 27001/9001 — so recommendations bolt onto management systems you may already run.
OWASP Top 10 for Agentic Applications (2026)
We threat-model your priority use cases against the agentic security top 10 — goal hijacking, prompt injection, tool misuse, identity & privilege abuse, weak guardrails, and the rest — because an agent doesn't just say the wrong thing, it can do it.
What you walk away with
Six concrete artifacts — the kind a CTO or CFO can take to a board to approve, or kill, an agentic-AI initiative.
Scored readiness scorecard
Your eight dimensions scored 1–5 (total /40) and mapped to a readiness band — benchmarked against external data, not a self-referential number.
Gap analysis
Current state vs target state for every dimension, naming the specific blocking issues — no generic “improve documentation”.
Prioritised intervention backlog
5–10 candidate agent use cases ranked by impact × effort × adoptability, with two to three worked candidates and value estimates.
Governance & risk register
Risks and recommended controls mapped to NIST AI RMF, ISO/IEC 42001 and EU AI Act risk tiers, plus an OWASP-agentic threat model for the priority use cases.
12–18 month sequenced roadmap
Quick wins → foundations → scaled rollout, each step carrying effort, cost, owner and dependencies.
Capability plan & ROI model
A team/skills/partnership plan plus a business case with stated assumptions and a measurement plan you can take to a board.
How it works — three weeks
Async data collection
Before we meet, we pull measurable baselines from your systems — git, CI/CD, project management, coverage — so findings rest on measured data, not vibes.
Discovery & mapping
Two working sessions to map how your team actually ships, Spec → Dev → Test → Deploy → Monitor, and capture change-readiness signals.
Analysis & architecture
We score the eight dimensions, rank interventions, model ROI and design the governance register — each finding carries a High/Medium/Low confidence rating.
Roadmap & readout
An executive readout and a sequenced build roadmap with both technical and adoption milestones — so the plan survives contact with Monday morning.
Our method: recommendations designed to be adopted
A recommendation nobody follows is worth nothing. So every intervention we propose is designed as a paved road — it shows up where your engineers already work, removes steps instead of adding them, and is default-on. Each one carries a trust ladder (shadow → assist → autonomous) with measurable, data-driven transition criteria, rather than a calendar. And we define adoption exit criteria up front, so “done” means the workflow is genuinely in use — not merely shipped.
This is also an honest description of scope: the assessment delivers a benchmarked diagnosis and a roadmap — not the implementation. The assessment stands alone and is valuable whether or not you go on to build with us.
Who it's for
Built for the people accountable for getting agents into production — not the curious bystanders.
Engineering leaders who already ship software
CTOs, VPs and Heads of Engineering — typically teams of roughly 6–200 — who want AI agents in production, not another proof-of-concept that stalls.
Teams tired of demos that never land
If you've seen impressive agent demos but nothing has stuck, the gap is usually governance, data and adoption — exactly what this assessment measures.
Leaders who need a decision-grade artifact
You need something benchmarked and costed to take to a board — to commit budget with confidence, or to decide not to.
£14,500, fixed
No hourly surprises. Three weeks.
Included
- Week 0 async data pull from your systems
- Discovery & mapping sessions
- Eight-dimension readiness scorecard
- Prioritised intervention backlog
- Governance & risk register (standards-mapped)
- 12–18 month sequenced roadmap
- Capability plan, ROI model & executive readout
Not included
- Implementation of the roadmap (a separate, optional engagement)
- Tool or platform licence costs
- Ongoing run/managed-service of any agent
The assessment is deliberately decoupled from any follow-on build, so the recommendations stay honest.
Who runs it
The assessment is led by Mabroor Ahmed, founder of M2N.IO, drawing on two decades of enterprise delivery experience — shipping and securing software in regulated, high-stakes environments, including work for KIA, Hyundai and Sainsbury's. The framework combines that delivery background with the recognised AI-governance and security standards above.
More about M2N.IOFind out where agents will actually land in your org
Book a short fit call to check the assessment is right for your team. If it isn't, we'll tell you.