Securing Agentic AI: Roadmap Part-10

DATE: 2025-12-07T20:48:00+04:00 SYSTEM: Agentic AI

Part 10. Implementation Roadmap

10.0 Why you need a roadmap, not a random pile of bots

You now have:

  • Agent patterns

  • Multi agent topologies

  • HITL designs

  • Threats and controls

  • Identity, architecture, governance

Great. Now the obvious question:

"So where do we start, and how far do we go?"

This part answers that in practical steps:

  • A maturity model so you know what level you are at

  • Phases that say what to build in which order

  • Build vs buy guidance

  • How to grill vendors without getting hand waved

End goal: you can sit with your CISO, CIO, and lead engineers and say:

"Here is how we will roll this out over 12 to 24 months without breaking the bank or the audit."

10.1 Maturity model

Think of this like an autonomy ladder. Not for cars. For agents touching your real systems.

Level 1 – Assisted

Human drives, agent suggests

Agents:

  • Only read data

  • Only suggest actions or content

  • Never call write tools directly

Examples:

  • Customer support agent that drafts replies

  • DevOps agent that suggests runbooks

  • KYC assistant that summarizes cases

Security posture:

  • Minimal blast radius

  • Easy HITL – humans already approve everything by default

  • Great place to learn how agents behave on your data

You are here if:

  • Agents do not have API keys for sensitive systems

  • Every change still goes through the main app or a human click

This is where almost every enterprise should start.

Level 2 – Supervised

Agent drives, human approves

Agents:

  • Can call write tools

  • Must pass through approval gates for high impact actions

Examples:

  • Payments agent that:

    • auto issues refunds up to 50

    • drafts refunds up to 200 for human approval

  • Infra agent that:

    • proposes restarts

    • runs them only after on call approves

Security posture:

  • HITL patterns from Part 4 are mandatory

  • Strong identity and scopes from Part 6

  • Tool gateway and policies from Part 7 active

You are here if:

  • You can point to concrete thresholds:

    • "Refunds up to 200 auto, up to 500 with approval, above that forbidden."

  • Your logs can show:

    • "Agent proposed, human approved, tool executed."

Level 3 – Autonomous with exceptions

Agent runs, human reviews outliers

Agents:

  • Execute a lot of actions without a human in the loop

  • Exceptions, anomalies, and higher risk paths trigger reviews

Examples:

  • Claims triage agent that:

    • auto handles simple claims under 300

    • flags edge cases or unusual patterns to adjusters

  • Fraud alert triage agent that:

    • closes obvious false positives

    • escalates uncertain cases

Security posture:

  • Strong anomaly detection and monitoring

  • Very clear thresholds and policies

  • Good replay tools for when decisions are questioned

You are here if:

  • You can show charts where 70 to 90 percent of volume is fully automated

  • There is a clear review workflow for the remaining 10 to 30 percent

Level 4 – Fully autonomous within hard bounds

Agent self manages inside strict policy fences

Agents:

  • Operate long running workflows

  • Coordinate other agents

  • Adjust their own behavior within policy

Examples:

  • Cost optimization agents that:

    • scale infrastructure up and down

    • commit changes within budget and safety limits

  • Large scale ops agents in manufacturing:

    • reroute orders

    • reschedule tasks based on machine status

Security posture:

  • Very strong governance

  • Very solid HITL on policy changes, not individual actions

  • Agent policies treated like rules in a trading engine or safety system

You are here if:

  • You trust your observability, testing, and kill switches enough that an agent having real authority does not keep you up at night.

  • Regulators and auditors understand and accept your control story.

Real Talk
Most enterprises should aim for Level 2 broadly, Level 3 on a few carefully selected flows, and only go to Level 4 in very narrow, well understood areas.

10.2 Phased adoption

Levels describe “how far”. Phases describe “in which order”.

You can map phases roughly to levels, but they are more about delivery steps.

Phase 1 – Single agent, single tool, shadow mode

Goal:

  • Prove value

  • Build trust

  • Build plumbing

Characteristics:

  • One agent

  • One meaningful tool

  • Shadow mode:

    • agent suggests

    • human executes

  • Strictly read first if possible

Example candidates:

  • Support email summarizer that:

    • reads the ticket

    • drafts the reply

    • agent never touches the ticket system directly

  • KYC summarizer that:

    • reads documents

    • writes a summary

    • never changes KYC status

Tasks in this phase:

  • Set up:

    • identity model

    • logging

    • trace ids

    • basic test harness

  • Agree simple governance:

    • manifests in Git

    • owner for the agent

    • approval for moving out of shadow mode

Success criteria:

  • Measurable time saved per case

  • Users still in control

  • No scary incidents in a few weeks of running

Executive Takeaway
Phase 1 is about learning on real data with low risk. If Phase 1 does not clearly help someone’s day job, stop and rethink the use case.

Phase 2 – Single agent, multi tool, HITL gates

(Usually Level 2)

Goal:

  • Let the agent actually do work

  • Keep humans in the approval loop for impact

Characteristics:

  • One agent

  • Several tools behind a gateway

  • HITL triggers from Part 4 active:

    • irreversibility

    • compliance

    • cost

  • Clear thresholds in code

Examples:

  • Banking:

    • CS agent can:

      • update contact details

      • raise tickets

      • trigger small refunds

  • DevOps:

    • SRE agent can:

      • read metrics

      • run diagnostics

      • propose restarts

      • only run restarts with on call approval

Tasks in this phase:

  • Build tool gateway with:

    • scopes

    • rate limits

    • detailed logs

  • Wire HITL with:

    • approval UI

    • timeouts

    • fallbacks

Success criteria:

  • Significant manual work removed

  • Approval workload still manageable

  • No unapproved high impact actions

Phase 3 – Multi agent, defined handoffs, exception review

(Bridge to Level 3)

Goal:

  • Use multiple specialized agents

  • Make handoffs safe and understandable

Characteristics:

  • Clear topologies from Part 3:

    • supervisor worker

    • pipeline

  • Context passing and trust rules defined

  • Exception based reviews for mature flows

Examples:

  • SaaS:

    • Search agent:

      • finds relevant tickets and docs

    • Analysis agent:

      • synthesizes answer

    • Execution agent:

      • applies changes in CRM with HITL for high risk changes

  • Banking onboarding:

    • Data collection agent

    • Sanctions and PEP screening agent

    • KYC summarizer agent

Tasks in this phase:

  • Implement:

    • agent to agent context formats

    • handoff authentication

    • state integrity checks

  • Extend tests:

    • multi hop prompt injection

    • trust chain attacks

Success criteria:

  • Agents hand off without losing context or leaking permissions

  • Errors and weird behavior traceable across the chain

Phase 4 – Complex orchestration, policy based autonomy

(Selective Level 3 and 4)

Goal:

  • Run higher scale, higher complexity workflows with:

    • policies

    • monitoring

    • strong governance

Characteristics:

  • Multi agent graphs

  • Policy engines guide:

    • which agent can do what

    • when HITL must happen

  • Agents manage their own branches within strict limits

Examples:

  • Manufacturing:

    • Scheduling agents

    • Maintenance agents

    • Supply chain agents

    orchestrated to respond to breakdowns and demand spikes.

  • Financial services:

    • Several agents:

      • research

      • risk

      • pricing

      • legal check

    assemble product offers within policy.

Tasks in this phase:

  • Integrate with:

    • policy engines

    • enterprise orchestration tools

  • Strengthen:

    • chaos testing

    • cost controls

    • multi tenant controls

Success criteria:

  • Complex flows fully automated for normal cases

  • Deviations caught early by monitoring and circuit breakers

Pattern Reference
Phases are per use case. You can have:

  • claims agent in Phase 3

  • DevOps agent still in Phase 2

  • a new marketing agent starting at Phase 1
    all at the same time.

10.3 Build vs buy analysis

You have three paths:

  1. Build your agent platform yourself

  2. Buy a managed agent platform

  3. Mix both

There is no single right answer, but there are wrong answers.

10.3.1 Build – frameworks like LangChain, LangGraph, AutoGen, CrewAI, custom

You use:

  • LangChain / LangGraph

  • AutoGen

  • CrewAI

  • OpenAI Swarm style patterns

  • Or a custom orchestrator

Pros

  • Full control over:

    • identity

    • network

    • data stores

    • logging

  • Easier to pass strict internal and local regulatory requirements

  • No surprise vendor agent crawling through your crown jewels

Cons

  • You own:

    • reliability

    • upgrades

    • debugging

    • security hardening

  • Needs strong internal engineering

Good indicators for building:

  • You already have:

    • mature platform engineering

    • a central AI platform team

    • strict data residency or on prem needs

Developer Note
If you already run K8s, service meshes, secret management, and internal SDKs, adding an internal agent SDK and runtime is very doable.

10.3.2 Buy – managed agent services

Examples:

  • Azure AI Agent Service

  • AWS Bedrock Agents

  • Google Vertex AI agents

  • Other commercial agent platforms

Pros

  • Faster initial delivery

  • Built in tools for:

    • conversation history

    • basic HITL

    • some safety filters

  • Less infra to run yourself

Cons

  • Harder to meet very strict controls:

    • on prem

    • custom identity

    • deep network segmentation

  • Integration into your specific tools and data might need work

  • You depend on vendor release schedules

Good indicators for buying:

  • You want to quickly stand up:

    • internal assistants

    • low risk agents for office tasks

  • Your main use cases are internal productivity, not core transactional systems yet

Real Talk
For mission critical flows that move money, open valves, or change access rights, most enterprises will still need custom control layers even if they use managed agents under the hood.

10.3.3 Hybrid – best of both, if you keep boundaries clean

Hybrid pattern:

  • Use managed agent tools for:

    • office assistants

    • generic productivity

    • small line of business helpers

  • Use in house agent platform for:

    • payment agents

    • KYC and AML

    • DevOps automation

    • anything touching regulated data or safety systems

Key is to:

  • Keep responsibilities clear

  • Do not let a vendor agent be the only layer of protection between your LLM and critical systems

Example hybrid:

  • Developers use a vendor assistant integrated into IDE for code help

  • Customer facing agents run in your cluster with internal tools and strong controls

  • Both share a common security pattern and threat model

10.3.4 Framework selection criteria

If you build with LangChain, LangGraph, AutoGen, CrewAI or similar, check:

  • Can it model the patterns you care about:

    • ReAct

    • Plan and execute

    • Multi agent graphs

  • Does it support:

    • explicit tool definitions

    • structured tool results

    • easy injection of your own auth and logging

  • Does it make it easy to:

    • intercept tool calls

    • record traces

    • plug in your observability

Security Warning
If a framework hides tool calls in ways you cannot intercept or log, that is a red flag. You want control, not magic.

10.4 Vendor and tool evaluation

If a vendor wants to sell you “Agent Platform X”, here is how you avoid a shiny trap.

10.4.1 Security questionnaire for agent platforms

Ask very specific questions like:

  1. Identity and access

    • How are agents identified in your system

    • How do you integrate with our IdP and RBAC

    • Can we enforce least privilege per agent and per tool

  2. Tool boundaries

    • How are tools defined

    • Can we restrict which agents can call which tools

    • Can we enforce our own parameter validation

  3. Data handling

    • Where is data stored, including conversations, traces, and memories

    • How is data classified, encrypted, and retained

    • How do we delete or anonymize data for specific users or tenants

  4. HITL and approvals

    • How does your platform support human approvals

    • Can we implement our own trigger logic

    • What is captured in the audit of an approved or rejected action

  5. Logging and monitoring

    • What logs and metrics can we export

    • Can we integrate with our SIEM and APM

    • Do you support trace ids we control

  6. Model and prompt management

    • How are prompts versioned

    • How do we test changes before they hit Prod

    • How are model updates handled and communicated

Executive Takeaway
If a vendor cannot answer these clearly, they are not ready for serious enterprise work, no matter how pretty the UI looks.

10.4.2 Red flags in agent tooling

Be cautious when you see:

  • “No code, just drag and drop, we take care of security”

  • Agents that can reach your internal APIs directly without a tool gateway in between

  • No way to export logs in a structured way

  • Prompts stored only in the vendor UI without version control

  • “We train on your usage by default” for sensitive workloads

And the big one:

  • The vendor gets annoyed when you ask about:

    • traceability

    • kill switches

    • incident response

Security Warning
Any agent platform that cannot explain how you shut an agent down quickly during an incident is not a platform you want in your core flows.

10.4.3 Reference architecture requirements for vendors

When you talk to vendors, show them your desired architecture from Parts 7 and 8 and see how they plug into it.

Minimum expectations:

  • Agents and tools can be called from within your VPC or private network

  • Your IAM controls who can use which agents and which tools

  • You control data residency and cross border movement

  • You can route all logs to your observability stack

  • There is a clear story for:

    • HITL

    • cost control

    • incident response

Ask them to map:

  • Their components

  • To:

    • your agent orchestrator

    • tool gateway

    • data stores

If the story sounds like “just send us all your data and APIs and we will handle everything”, pass.

10.4.4 Real world vendor evaluation scenario

Imagine you are a regional bank.

Vendors A and B pitch agent platforms.

Vendor A says:

  • “Connect us to your core, we have prebuilt banking agents.”

  • Logs stay mostly in their cloud, with limited export.

  • HITL is built in, but approvals and logs cannot be easily integrated with your existing systems.

Vendor B says:

  • “Our system runs inside your Kubernetes clusters.”

  • Tools are your own HTTP endpoints behind your API gateway.

  • You own:

    • logs

    • identity

    • approvals

Vendor B is clearly closer to what Parts 6 to 9 described.

You still need to check their quality, but at least your control story is intact.

10.5 Pulling it together

To turn this entire guide into a concrete plan, one possible path looks like this:

  1. Next 30 to 60 days

    • Pick 1 or 2 Level 1 use cases:

      • KYC summarizer

      • CS email summarizer

    • Stand up:

      • identity context

      • tool gateway skeleton

      • basic logs and metrics

  2. Next 3 to 6 months

    • Move one or two use cases to Level 2 with strong HITL:

      • small refunds

      • simple infra actions

    • Establish:

      • agent registry

      • CI tests and red team suite

      • incident runbooks and kill switches

  3. Next 6 to 12 months

    • Add multi agent flows for complex cases:

      • onboarding

      • internal research

    • Refine:

      • monitoring

      • cost controls

      • cross agent handoffs

  4. 12 months and beyond

    • Carefully introduce Level 3 autonomy in narrow, well understood flows

    • Consider Level 4 autonomy only where:

      • risk is limited

      • controls are mature

      • regulators understand the setup

Real Talk
You do not need to boil the ocean. You do need to treat every agent that touches real systems as a product, with owners, tests, and controls.


Closing Note: Autonomy, Probabilities, and Human Brains

Current agentic AI is built on probabilistic foundations. Underneath all the fancy orchestration, tools, and multi agent graphs, there is still a model that is making its best guess at the next token. Until the core behavior gets closer to deterministic, complete, unsupervised autonomy in high stakes environments will be very hard to trust.

Think about it this way: if we start talking about berries right now, what comes to mind for you? Strawberries, blueberries, something you ate this week. Humans are also probabilistic in how we recall and respond, but we are not only that. We have timelines. We have lived experiences. We have the ability to say “this feels wrong, I am going to stop here” even when the pattern suggests otherwise.

We spend our entire lives learning from the moment we show up on this planet. We accumulate memories, build abstractions, generalize from a few painful edge cases, and carry those lessons forward. When something goes badly once, most people do not need to run that experiment ten more times to believe it.

Agentic AI systems do not work like that yet. They stack a probabilistic model on top of tools, workflows, and memory stores, but they do not really have experience in the human sense. They have logs. They have state. They have patterns in embeddings. Given the datasets we feed them and the architectures we deploy them in, they can be incredibly useful, but they do not suddenly become artificial colleagues with human style judgement just because we wrapped them in an “agent” abstraction.

The gap is not only technical. It is architectural. We are trying to approximate something that evolved over millions of years using systems that are, at their core, very capable pattern matchers wrapped in planning loops and tool calls. That can be powerful. It can absolutely transform workflows and productivity. It just is not a drop in replacement for human decision making in the places where accountability, ethics, and context really matter.

That is why this guide leans so hard on identity, HITL, guardrails, governance, and clear boundaries. Agentic AI is worth using, but it is not magic. If we treat it as a set of powerful but probabilistic components that need structure and oversight, we get real value with controlled risk. If we pretend it is already a fully reliable autonomous colleague, we are lying to ourselves and setting up some very expensive lessons.

 

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