AI Technology
AI Agents Explained: Models, Frameworks and Real Uses in 2026
A practical guide to AI agent models, frameworks and protocols, with bounded recruitment uses, approval points and official technical sources.
Published 30 April 2026 · Substantially reviewed 9 October 2026 · 12 min read
The four layers people often call an AI agent
An agent is a system, not a model name. Anthropic distinguishes fixed workflows from agents that dynamically direct their own process and tool use. That distinction matters because each extra degree of freedom adds cost, latency and another failure path.
Model
Produces structured output, text or tool requests. Model choice changes capability, latency and cost, but it does not create permissions or governance.
Framework or runtime
Maintains state, executes the loop, routes tool calls, records traces and applies retries or stopping rules. A small application can do this without a large framework.
Protocol
Defines an exchange boundary. MCP connects applications to context and tools. A2A describes agent-to-agent discovery and task exchange. Neither protocol is a security certification.
Application controls
Authentication, authorisation, validation, audit logs, budgets, evaluation and human approvals determine what the system is actually allowed to do.
Choose the least complex pattern that works
| Pattern | Use it when | Recruitment example | Control boundary |
|---|---|---|---|
| Single model call | One bounded transformation | Draft a job-ad variant | Human reviews the output |
| Fixed workflow | Known steps and predictable branches | Prepare a shortlist pack from approved records | Human approves the pack and every external action |
| Single agent | The order of research or tool use varies | Investigate a database-quality issue | Read-only tools first; approval before writes |
| Multiple agents | Independent specialist work can be evaluated separately | Research separate talent markets | Named owner reviews evidence and synthesis |
A framework can speed up the common plumbing, but it adds abstraction too. Keep prompts, tool schemas, state transitions and raw traces inspectable. Add multi-agent orchestration only when separate workstreams have clear inputs, outputs and evaluation criteria.
What tool use really means
With custom function calling, the model proposes a function name and arguments. Your application validates the request, decides whether it is permitted, executes the code and returns the result. Google’s official function-calling guide explicitly assigns execution to the application. The model doesn’t gain authority just because it can format a tool request.
For recruitment data, start with narrow read tools. Separate preparation from commitment. Require a person to approve outreach, candidate status changes, record merges, exports and any recommendation that could affect access to work. AI recruiting agents should prepare the work and leave the commitment to a recruiter.
MCP and A2A solve different boundaries
MCP: application to context and tools
The official MCP architecture uses a host, clients and servers. Servers can expose tools, resources and prompts over local or remote transports. Review each server’s operator, scopes, data path and outputs before enabling it.
A2A: agent to agent
The A2A specification describes messages, tasks, artifacts and capability discovery between independent agents. It can support long-running work, but production systems still need authentication, authorisation, traceability and an escalation path.
Bounded recruitment uses
Database review
Flag possible duplicates or conflicting facts with links to source records. A recruiter decides whether records should be merged or corrected.
Search preparation
Draft search terms and explain why each criterion relates to the brief. A recruiter approves the criteria and reviews the resulting pool.
Candidate summaries
Prepare a factual summary from authorised records and show provenance. A recruiter checks omissions and makes the assessment.
Communication drafts
Draft a message using approved facts and tone. A recruiter confirms the recipient, content and timing before anything is sent.
Handover preparation
Collect open actions, evidence and unresolved questions. The incoming recruiter confirms ownership and the next step.
Evaluation before autonomy
- Define a narrow task, accepted evidence and prohibited actions.
- Replay representative cases, including missing data and conflicting instructions.
- Measure factual accuracy, tool-selection accuracy, approval compliance, latency and cost.
- Set retry, spend and time limits, then test the escalation path.
- Review traces with recruiters before expanding permissions.
Anthropic’s current trustworthy-agent guidance centres human control, transparency, secure interactions and privacy. In practice, people choose which tools are enabled and which actions always require approval.
Official sources used
Frequently asked questions
What is an AI agent?
An AI agent is an application in which a model directs part of a task, chooses from permitted tools, observes results and decides what to do next. The application still supplies permissions, stopping rules, records and human approval points.
What is the difference between a model, a framework and a protocol?
The model generates and reasons. A framework is application code that manages state, tool calls, retries and traces. A protocol defines how separate components exchange capabilities or work. They solve different layers of the system and are not interchangeable products.
What is MCP?
The Model Context Protocol is a client-server protocol for exchanging context. Its official architecture defines tools, resources and prompts, plus transports for local and remote connections. MCP compatibility does not by itself establish that a server is trustworthy or appropriately authorised.
What is A2A?
The Agent2Agent protocol defines how independent agents can discover capabilities, exchange messages and manage work represented as tasks and artifacts. It complements tool protocols, but an implementation still needs identity, authorisation, logging and failure handling.
Do recruitment teams need autonomous agents?
Usually not at the start. A fixed workflow is easier to test when the steps are known. Use model-directed behaviour only where the task is genuinely variable, and keep candidate decisions, outbound communication and record changes behind explicit human approval.
Which recruitment tasks are sensible first uses?
Low-risk preparation is the clearest starting point: summarising notes, identifying missing fields, drafting a search strategy or preparing a message for review. A recruiter should verify source records and approve anything that affects a person or an external system.
Evaluate an agent around the decision it supports
Start with a real workflow, controlled records and named approval points. FluxHire.AI is developing reviewable recruitment workflows where people remain responsible for consequential decisions.
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