The Programming Language for Agentic Software

Every era of computing develops its own programming language.

The mainframe era had COBOL and Fortran. The systems era had C. The web era had JavaScript and Python. Each emerged for the same reason, the previous generation could no longer express the new abstraction.

We are now in the agentic era.

Software is no longer just executing predefined instructions. It is reasoning over context, calling tools, retrieving knowledge, learning from past runs, and making decisions at runtime.

When the contract of software changes, the language must change too.

What makes a programming language?

A programming language is made of three things:

  1. Primitives to think and build with.
  2. An engine to execute those primitives.
  3. A runtime that governs memory, I/O, permissions, and interaction with the outside world.

An SDK alone is not a programming language. A collection of utilities is not a programming language. Without an execution engine and a runtime that enforces behavior, you have a library, not a language.

Python gives you lists, functions, and classes. Its interpreter runs them. Its runtime manages memory, exceptions, and interfaces with the operating system.

React gives you components and state. Its reconciler computes updates. The browser handles rendering and events.

Applying this to agentic systems:

  • Agno gives you agents, teams, workflows, memory, knowledge, tools, guardrails, and approval flows.
  • The Engine runs them: model calls, tool execution, context construction, and iteration.
  • AgentOS, the production runtime, governs execution and interfaces with the outside world via an API: streaming, request-level isolation, authentication, RBAC, monitoring, background execution.

The runtime is stateless. Sessions, memory, state and traces persist in your database. Permissions are enforced at request boundaries.

Agno provides the SDK + Engine + Runtime for agentic software.

Agents are the new programs

Traditional applications are collections of deterministic programs. Every path is written in advance. The system does exactly what the developer specified.

Agents change that.

An agent reasons over context. It chooses tools dynamically. It retrieves knowledge. It remembers previous runs. It decides which path to take at runtime.

This is still software, but the path between input and output is no longer fixed.

This does not mean deterministic systems disappear. For many workloads, static pipelines are faster, cheaper, and more reliable.

But when the system must pause, reason, retrieve, and adapt dynamically, predefined control flow breaks down.

For decades, the contract was simple:

Same input, same output.

Agentic software breaks that contract.

The same input can produce different outputs depending on memory, context, retrieval, and prior state. If execution is dynamic, the language must express that natively.

Agentic software needs a new contract

Agentic software requires new capabilities built into its programming language:

1. A new interaction model

Static software receives a request and returns a response.

Agentic software streams reasoning, tool calls, intermediate results, and pivots in real time. The execution path can change mid run, or pause for days. The system may retrieve knowledge halfway through and completely redirect its reasoning.

Streaming and iteration are the default and the language for agentic software must treat them as first class behavior.

2. A new governance model

Traditional systems execute predefined decisions within rules written in advance. Code does not decide whether to send an email or issue a refund. It simply follows instructions.

Agents make decisions, and not all decisions are equal.

Some actions are low risk: summarizing text or searching documentation. Some require user approval: sending emails or booking travel. Some require admin approval: issuing refunds, deleting records, changing permissions.

Without runtime-enforced approval boundaries, an agent that can draft an email can also execute a payment. The difference must be enforced by the runtime, not prompt engineering.

Governance must be part of the agent definition itself and the runtime must enforce it.

3. A new trust model

Static systems are trusted because every path is written in advance.

Agents introduce probabilistic reasoning into the execution path.

If guardrails and evaluation run outside the runtime, they are advisory rather than enforceable. Unsafe output can be produced before policy checks intervene.

Trust must therefore be part of the runtime semantics: guardrails, evaluation, logging, pre and post-response checks integrated into execution.

Interaction. Governance. Trust.

These are language-level concerns in the agentic era.

What this looks like in practice

Here is a lightweight coding agent that writes, reviews, and iterates on code. It remembers project conventions, retrieves knowledge, learns from past runs, and operates within explicit governance boundaries.

This example is intentionally minimal but production-capable. It has persistence, memory, learning, and controlled tool execution.

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses
from agno.tools.coding import CodingTools
from agno.tools.reasoning import ReasoningTools

gcode = Agent(
    name="Gcode",
    model=OpenAIResponses(id="gpt-5.2"),
    db=SqliteDb(db_file="agno.db"),
    instructions=instructions,

    # Knowledge: searchable long-term memory
    knowledge=gcode_knowledge,
    search_knowledge=True,

    # Learning: extract and store learnings over time
    learning=LearningMachine(
        knowledge=gcode_learnings,
        learned_knowledge=LearnedKnowledgeConfig(mode=LearningMode.AGENTIC),
    ),

    # Tools: controlled extensions
    tools=[CodingTools(base_dir=workspace, all=True), ReasoningTools()],

    # Memory: learn user preferences
    enable_agentic_memory=True,

    # Context: include prior runs
    add_history_to_context=True,
    num_history_runs=10,
    markdown=True,
)

Notice what is being defined:

  • Knowledge as a first class primitive
  • Learning as a built in capability
  • Tools as controlled extensions
  • Memory and historical context as defaults
  • A runtime that governs how the system executes

These are not utilities or third party integrations. They are the vocabulary of the agent and enforced by the runtime and execution layer.

That is what a programming language does. It gives you the right primitives for the era you are building in. You define the behavior. The language enforces it.

Every era gets the language it needs

COBOL abstracted business logic away from assembly. C abstracted system engineering without hiding it. Python abstracted memory management and low level primitives to accelerate iteration.

Each language captured the dominant abstraction of its era.

The agentic era introduces a new abstraction: systems that reason, remember, and decide at runtime.

The contract has changed. The primitives have changed. The execution model has changed.

The language must change too. That language is Agno.

There are many that argue that because Agno is written in Python, it cannot be a programming language.

If you wish to make an apple pie from scratch, you must first invent the universe. — Carl Sagan