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Agentic AI Developer

Builds systems where a model plans, calls tools and acts across steps, rather than answering one prompt at a time.

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The role

What the job actually is.

An agentic developer writes the layer between a model and the systems it is allowed to touch. That means tool definitions, the loop that decides what to call next, and the boundaries on what the agent may do without a human. Most of the work is not the happy path: it is what happens when a tool times out, returns something unexpected, or succeeds on step four of six. The role sits closer to distributed-systems engineering than to data science, and the people who are good at it usually came from one of those two directions rather than from prompt writing.

What we screen for

The questions that separate the field.

Asked by someone who has built the thing, and designed to catch this role's specific failure rather than to confirm a general impression.

  1. Evidence of a tool-calling system in production, not a demonstration. Ask which tools, called by what, and who could see the results.
  2. How they bounded the agent's authority. Which actions needed a human, and how that gate was enforced rather than requested.
  3. What the system does when a step fails halfway. A specific incident, not a policy.
  4. Whether they can describe a retry, a rollback and an audit trail for the same run.

The common mis-hire

The one you have probably already made.

A strong application developer who has wired one API call to a model and calls the result an agent. No failure semantics, no evaluation, and no answer to what the system does when the tool times out. The build demonstrates well and then cannot be given permission to touch anything that matters.

In the estate

Where this role works, and what we screen it against.

The layers this family works at, lit. These are the tools we screen against. Naming one says we can test for it, not that we have delivered on it.

Evaluation and observability

Spans every layer. Without it a system is shipped on impressions.

Role families we place here

Experience and delivery

Copilots and agents inside a business process, and the interaction design that makes an uncertain system usable.

  • Salesforce Agentforce
Role families we place here

Orchestration and agents

Where an agent's steps, tools and state are defined, and where its failures are caught before a user meets them.

Also screened against

  • LangChain
  • LangGraph
  • CrewAI
  • AutoGen
  • Semantic Kernel
  • Model Context Protocol
  • Pydantic AI
  • n8n
Role families we place here

Models

The models themselves, and the platforms an enterprise hosts them through.

  • Anthropic (Claude)
  • OpenAI
  • Google (Gemini)
  • Azure AI Foundry
  • AWS Bedrock
Role families we place here

Data and grounding

What the model is grounded in, and the integration work that gets enterprise data to where it can reach it.

  • Pinecone
  • MuleSoft

Also screened against

  • pgvector
  • LlamaIndex
Role families we place here

Systems you already run

The seven platform desks Yallo staffs. Almost no AI work is greenfield; it lands here.

Role families we place here

Governance, risk and safety

Spans every layer. Named as what governance roles are screened against; what any of them obliges is your counsel's call.

  • EU AI Act
  • ISO/IEC 42001
  • ISO/IEC 23894
  • NIST AI Risk Management Framework
  • OWASP Top 10 for LLM Applications
Role families we place here
Naming a technology here says we screen against it, not that we have delivered on it. The role families on each layer are the ones we place there.

Seniority

What changes between mid, senior and lead.

The grade is a description of what the person owns, not a band. Rates come with the shortlist.

Mid
Implements tools and flows against an agreed design. Needs the authority boundaries and the failure handling specified for them.
Senior
Owns the loop design and the failure semantics. Decides what the agent may do unattended and defends that line in a security review.
Lead
Sets the pattern across several agents, owns the evaluation and rollback story, and is the person who says an agent is not ready to be given write access.

In a programme

When this role is needed, and what blocks it.

Arrives once the target architecture is agreed and the systems the agent will call are known, which in practice means after the solution architect and alongside the integration work rather than before it. The dependency that catches programmes out is authorisation: an agent that acts needs an identity and a permission model, and that is owned elsewhere. Retain into hypercare, because agent behaviour changes when the underlying model does.

Adjacent

The roles this one is confused with.

Ask

Send the brief, get a screened Agentic AI Developer shortlist.

Tell us the programme, the stack and the timeline.

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