#4439 AI Agents
#4439 AI Agents
#4439 I don't trust AI agents to be deterministic, therefore I cannot predict what they will do, and they can do damage without intervention.
That is a completely valid, deeply rational stance to take. In fact, if you approach systems engineering from a safety-critical mindset, treating an autonomous LLM agent as entirely deterministic is a massive, high-risk error.
By their very nature, large language models are probabilistic, not deterministic. Because they operate on token probabilities rather than rigid, hard-coded logic paths, they introduce non-determinism into execution. When you wrap that probabilistic engine in an autonomous, agentic loop—giving it tools to execute code, read/write to databases, or call APIs—you are essentially creating a system with a massive, unpredictable state space.
Without strict guardrails and human intervention, non-deterministic agents running in autonomous loops can and will break things.
If you want to leverage the reasoning power of agents without giving up predictability and control, you have to design for it from the ground up. Here is how you can systematically de-risk agentic workflows:
1. The "Human-in-the-Loop" (HITL) Gate
The simplest and most non-negotiable rule for high-stakes agents is the execution gate.
The Pattern: The agent can reason, plan, and stage actions, but it cannot execute them.
The Reality: Instead of letting an agent run a database mutation or an API call directly, the agent outputs a proposed payload (e.g., a structured JSON command) to a staging queue. A human must review and explicitly approve the action before it hits production.
2. Hard-Coded Deterministic Guardrails
Never let an agent's prompt be the only thing keeping it inside the lines. You must wrap the non-deterministic agent inside a rigid, deterministic software container.
Constrained Action Spaces: Use strict schema validation (like Pydantic) to force the agent to only communicate in precise JSON schemas. If the agent tries to hallucinate a tool or an invalid parameter, the deterministic parser rejects it before execution.
Runtime Sandboxing: If an agent is writing or executing code, it must happen in an entirely isolated, ephemeral sandbox (like a secure Docker container) with zero access to your local file system, internal networks, or sensitive environmental variables.
3. Finite State Machine (FSM) Orchestration
Instead of giving an agent a blank slate to "go solve this problem," constrain its trajectory using a deterministic state machine.
Rather than using a fully autonomous agent loop, break the workflow into explicit, discrete states (e.g.,
State A: Ingest,State B: Parse,State C: Validate).The agent is only used within a single state to perform a specific, isolated cognitive task. The transition to the next state is governed by hard-coded, deterministic routing logic, not the agent's whim.
4. Reducing Temperature (The Baseline)
At the lowest level, setting the model's temperature to 0 reduces the variance of the token sampling, forcing the model to choose the most mathematically probable token. While this makes the model more consistent, it's vital to remember that Temperature 0 is still not 100% deterministic due to the underlying architecture of parallel GPU computing and floating-point variations, but it cuts down on wild unpredictability.
The Bottom Line: You shouldn't trust them to be deterministic, because they aren't. The goal isn't to fix the agent's inherent non-determinism, but to build an engineering architecture around it that ensures safety, predictability, and absolute control.
How are you currently structuring your workflows? Are you looking to build a specific containment architecture or runtime sandbox to keep these models on a short leash?
What are the various agent frameworks?
To handle the inherent non-determinism of large language models, the industry has shifted away from a "just pick a tool and build" mentality toward a strict, architectural choice of frameworks.
Because you cannot inherently trust an LLM to be deterministic, these frameworks are designed to wrap probabilistic models in engineering containers. They generally fall into three distinct architectural categories based on how they attempt to control execution.
1. State-Machine / Graph-Based Frameworks
Best for: Strict engineering control, predictable execution paths, and self-correcting loops.
Instead of letting an agent decide what step to take next in a black box, these frameworks force the workflow into a rigid mathematical graph (nodes and edges).
LangGraph (by LangChain)
The Architecture: Built entirely around the concepts of Nodes (executable functions/agents), Edges (conditional routing logic), and a centralized, thread-safe State.
It uses first-class persistent checkpointing. How it handles Control: It allows for non-linear execution, meaning you can build a cycle where an agent attempts a task, a deterministic validator node checks the output, and if it fails, loops back to the agent to retry.
Human-in-the-Loop: Outstanding.
You can place a "breakpoint" on any edge, which pauses the state machine entirely, serializes the state to a database, and waits for a human to review, edit, or approve the state payload before resumption.
2. Conversation-Native / Multi-Agent Debate Frameworks
Best for: Code execution, multi-perspective verification, and enterprise-grade messaging.
These frameworks treat collaboration as an asynchronous event stream or structured group chat.
AutoGen (Microsoft)
The Architecture: An event-driven, conversation-centric framework. It features built-in
AssistantAgenttypes and a crucialUserProxyAgent.How it handles Control: AutoGen excels at multi-agent debate and validation.
For example, you can have a "Coder Agent" write a script, and a separate "Code Reviewer Agent" critique it before execution. Sandboxing: AutoGen natively prioritizes security via docker-based execution. Its
UserProxyAgentcan automatically route generated code to an isolated Docker container to execute safely, reporting the terminal output back to the swarm without risking the host system.
3. Role-Based / Pipeline Frameworks
Best for: Rapid prototyping and translating human business processes into sequential steps.
These frameworks use a highly intuitive metaphor: treat AI agents like employees with specific job descriptions, tools, and a hierarchy.
CrewAI
The Architecture: You define Agents (with specific roles, backstories, and goals), Tasks (with strict inputs and expected JSON outputs), and a Crew (the orchestrator).
How it handles Control: It manages execution either sequentially (Agent A passes work to Agent B) or hierarchically (a Manager Agent reviews and delegates work).
The Caveat for Safety: While incredibly fast to prototype, CrewAI is abstract and can be more opaque to debug when things go wrong under the hood compared to LangGraph’s explicit control graphs.
4. Integration & Semantic Bridges
Best for: Injecting AI into legacy enterprise infrastructure or native compilation.
Semantic Kernel (Microsoft)
The Architecture: A native SDK (heavily utilized in C#, Python, and Java) designed to bridge traditional, deterministic software with LLMs.
How it handles Control: Rather than giving an agent free rein to execute complex multi-turn reasoning, Semantic Kernel uses "Planners."
A Planner looks at an LLM prompt, maps it against a rigid list of native code plugins (C# functions, SQL commands), and sequences them. It treats the LLM as a strictly bounded translation layer between natural language and native code.
LlamaIndex (Agentic RAG)
The Architecture: Data-centric rather than orchestration-centric.
How it handles Control: It is built entirely to minimize hallucinations by strictly grounding agents in data retrieval pipelines. Instead of letting an agent browse the web or guess facts, LlamaIndex forces the agent to interact with a heavily structured index (like a Knowledge Graph or Vector Database) via deterministic query routing.
The Architectural Choice Matrix
| Framework | Core Philosophy | State Management | Best Use Case |
| LangGraph | Graph-based state machine | Centralized, persistent, version-controlled | Critical workflows requiring loops, retries, and strict HITL gates. |
| AutoGen | Conversational swarms | Distributed via chat history | Multi-agent debate, collaborative problem solving, and sandboxed code execution. |
| CrewAI | Role-based production lines | Task-context delegation | Linear business automation pipelines (e.g., Ingest $\rightarrow$ Analyze $\rightarrow$ Report). |
| Semantic Kernel | Native function plugin architecture | Local context variables | Embedding AI routing directly into existing strongly-typed enterprise applications. |
If your primary concern is preventing an agent from doing damage, LangGraph offers the most deterministic containment because you map out the bounds of the sandbox explicitly with code, rather than relying on the agent to behave based on a prompt.
Are you looking to evaluate one of these for a specific runtime environment, or are you trying to build a custom orchestrator from scratch?
What are the various workflows of an agentic team?
When you move from a single agent to an agentic team, the goal is to shift from a broad, error-prone "do-it-all" prompt to a system of highly specialized, narrow execution blocks. By dividing labor, you can enforce deterministic checks between steps, ensuring that a non-deterministic output from one agent is verified before it moves to the next.
In production engineering, multi-agent coordination generally follows one of five core architectural workflow patterns.
1. Sequential Pipeline (The Assembly Line)
How it works: A strict, linear workflow where the output of Agent A becomes the direct input for Agent B. There are no branches or cycles; execution moves forward deterministically.
The Control Mechanism: Between each agent node, a data validator (using hard-coded logic like Pydantic or regular expressions) parses the output. If Agent A’s output fails the structural validation, the pipeline halts immediately rather than passing corrupted data down the line.
Best Used For: Automated reporting, data transformations, and document ingestion pipelines (e.g., Ingest PDF --> Extract Schema --> Format Markdown).
2. Hierarchical / Supervisor Pattern (Command & Control)
How it works: A central "Supervisor" agent acts as the project manager. It receives the overarching goal, breaks it into discrete sub-tasks, delegates those tasks to specialized "Worker" agents, and aggregates the results.
The Control Mechanism: Worker agents cannot talk to each other; they can only talk to the supervisor. Crucially, the supervisor must evaluate the quality of a worker's output. If a worker fails its specific task, the supervisor can reissue the command with feedback.
The Risk Factor: The supervisor agent itself is an LLM making routing decisions. In safety-critical contexts, this supervisor node is often replaced or augmented by a deterministic router (like a Python
if/elseblock mapping to explicit intent keywords) to eliminate routing hallucinations.
3. Bidirectional Debate / Critic Pattern (Peer Review)
How it works: Two or more agents with adversarial roles work together. Typically, a Generator Agent produces a draft (code, analysis, or a system plan), and a Critic Agent audits it against a strict set of constraints or compliance standards.
The Control Mechanism: This creates an isolated execution loop. The Generator creates an artifact; the Critic evaluates it. The loop continues until the Critic issues an explicit "Approval" token, or a pre-determined maximum loop count (e.g., max 3 iterations) is hit to prevent runaway execution costs.
Best Used For: Automated code generation, requirement mapping verification, and syntax checking.
4. Orchestrator-Workers (The Dynamic Fan-Out)
How it works: The orchestrator agent analyzes a complex, multi-faceted problem and dynamically spawns multiple worker tasks in parallel.
The Control Mechanism: Unlike the hierarchical pattern where workers execute sequentially based on manager whim, the Orchestrator maps out an entire batch operation simultaneously. The framework waits for all parallel operations to complete (a code-level join barrier) before allowing an aggregator node to compile the final state.
Best Used For: Sub-component analysis, large-scale code refactoring across multiple files, and parsing massive regulatory documents simultaneously where sections can be analyzed independently.
5. Web/Graph Network (The Autonomous Swarm)
How it works: Agents are configured as independent peers in a decentralized network. They communicate via an event bus or a shared chat room, dynamically passing control based on who has the appropriate tools for the current state of the conversation.
The Control Mechanism: This is the most non-deterministic and unpredictable pattern. In enterprise or high-stakes engineering, this pattern is rarely allowed to run naked. Instead, it is constrained within a Finite State Machine (FSM). The agents can talk freely inside a state, but they cannot trigger a transition to a new phase of the project without passing a deterministic code gate or hitting a Human-in-the-Loop breakpoint.
Workflow Comparison Matrix
| Pattern | Determinism Level | Architectural Complexity | Primary Failure Mode |
| Sequential | High | Low | If one link breaks, the entire chain halts. |
| Hierarchical | Medium | Medium | Supervisor can hallucinate delegation or get stuck in loop. |
| Debate / Critic | Medium-High | Medium | Infinite looping if the Critic's criteria are too rigid. |
| Orchestrator-Workers | Medium | High | Fan-out handling fails if one worker times out or crashes. |
| Web / Swarm | Low | High | State explosion; highly unpredictable execution paths. |
If you are designing a system where predictability is paramount, the Sequential Pipeline wrapped in validation logic, or a Debate Pattern governed by a strict maximum loop counter, provides the safest boundaries.
Are you looking to orchestrate an agentic team where the agents interact autonomously, or are you prioritizing a layout where human review is mandatory between every single handoff?
I am aiming to develop my own in-house agentic framework with the various options
Building your own in-house agentic framework is the ultimate way to achieve complete control over non-deterministic systems. By rolling your own architecture, you strip out the bloated abstractions of generic third-party packages and replace them with strict, predictable software engineering boundaries.
To build a production-grade custom framework that treats the LLM as a modular engine rather than an unconstrained driver, your architecture must be built upon four foundational pillars.
Pillar 1: The Append-Only Immutable State
When agents fail or diverge, debugging a "live" mutable state object is a nightmare. Your custom framework should treat state as an immutable, versioned sequence of snapshots.
[State v1: Input] ──> (Agent Node) ──> [State v2: Proposed Action] ──> (Validator) ──> [State v3: Executed]
Implementation: Use Python
dataclassesorPydanticmodels that are structurally protected from direct overwrites. Every state transition should produce a distinct, timestamped snapshot saved to an in-memory data store (like SQLite or Redis).The Safety Payoff: If an agent acts erratically, you don't just see the failure—you can exactly replay the state sequence up to the failure point, or programmatically roll back the environment state to v_{n-1} to trigger an alternative recovery path.
Pillar 2: Bounded Finite State Machine (FSM) Orchestration
Never let the LLM determine the routing flow of your program through raw text strings. Instead, write your central coordinator as a hard-coded, deterministic FSM loop.
from enum import Enum
from pydantic import BaseModel
class SystemState(Enum):
INGEST = 1
ANALYZE = 2
VALIDATE = 3
HUMAN_GATE = 4
EXECUTE = 5
class WorkflowContext(BaseModel):
state: SystemState
payload: dict
iteration_count: int = 0
max_iterations: int = 3
The Execution Loop: The core engine is a standard Python
whileloop or event listener that evaluates theSystemState.Isolated Calls: When entering
SystemState.ANALYZE, the engine passes only the data needed for that step to the LLM. The LLM returns its response, but the code—not the model—evaluates whether to move toVALIDATEor cycle back. Ifiteration_count >= max_iterations, the code forcibly routes the system toHUMAN_GATEto prevent an expensive, runaway infinite loop.
Pillar 3: Decoupled Tool Execution via MCP Primitives
One of the fastest ways an agent causes damage is when tool definition is coupled directly to the agent's environment. If the agent hallucinates arguments or bypasses system permissions, it can compromise files or execution layers.
To de-risk this, adopt the Model Context Protocol (MCP) design primitives for your framework's tool abstraction layer:
Tools (Actions): Executable functions that the model can request to run. The model never runs them directly. It outputs an execution request schema. Your local runtime engine intercepts this request, validates the parameters against your schema, and executes it in an isolated scope.
Resources (Data): Structured read-only access points (e.g.,
graph://node_idorfile://path). This creates a hard architectural split: if the agent needs information, it calls a Resource (read-only). It cannot accidentally modify a resource because it lacks a write API interface.
The Runtime Sandbox
For tools that execute dynamic logic (like running Python scripts or shell tasks), your framework's tool runner must invoke these actions inside an ephemeral, containerized environment (such as a local Docker container or a distinct gRPC process with restricted permissions) completely segregated from your primary server environment.
Pillar 4: The Out-of-Band Human approval Gate
A true safety-critical framework treats the human review gate as a hard infrastructure break, not just a prompt instruction.
[Agent Task Completed] ──> [Serialize Context to Disk] ──> [Halt Execution Thread]
│
(Awaiting User Input)
│
[Resume Execution Thread] <── [Deserialize Context & Verify] <───┘
How to build it: When the FSM transitions to
SystemState.HUMAN_GATE, the currentWorkflowContextis serialized to a local database, and the execution worker thread gracefully yields and terminates.An external interface (a local CLI, a lightweight dashboard, or a messaging hook) surfaces the proposed action payload to you. Once you hit approve, a fresh worker thread pulls the serialized state from the database, checks its cryptographic checksum or state integrity, and passes it cleanly into
SystemState.EXECUTE.
The Core Blueprint of Your Custom Framework
When stitching these four components together into your code layout, organize your repository by clean separations of concern:
│
├── core/
│ ├── fsm.py # The deterministic state machine and execution loop
│ └── state.py # Immutable Pydantic models for step snapshot tracking
│
├── agents/
│ ├── prompt_templates.py # Specialized system personas (Planner, Critic, etc.)
│ └── inference.py # Local LLM connector (Ollama, local API endpoints)
│
└── tools/
├── registry.py # Schema validator for permitted execution actions
└── sandbox.py # Isolated container/gRPC runtimes for active tools
By keeping the architecture decoupled—where the state tracking is immutable, the execution loops are strictly hard-coded Python, and the tools run in sandboxed environments—you effectively eliminate the risk of a non-deterministic model hijacking your system infrastructure.
As you begin mapping out your core/fsm.py file, are you planning to drive your state engine using an asynchronous event-driven layout (like an event bus), or do you prefer a straightforward synchronous step-by-step pipeline layout?
In developing an agent framework, does it make sense to create and use interface agents to enable good communications from one agent to the next?
Yes, it makes complete sense, but with a critical architectural caveat: the "interface agent" should not be a free-form, conversational AI. If you use a standard, non-deterministic LLM to translate text between two other non-deterministic LLMs, you introduce a massive telemetry blind spot and compound the risk of error. This is known as "error propagation"—where minor hallucinations in the interface agent cascade into major failures down the pipeline.
Instead, in a robust, custom framework, an Interface Agent should be designed as a hybrid semantic-deterministic adapter. Its sole job is to translate unstructured cognitive thoughts from an upstream agent into a rigidly defined, machine-readable protocol (like a strict JSON schema or a strongly-typed graph mutation payload) that the downstream agent is hard-coded to accept.
Here is how you can effectively design and implement interface agents within your custom framework to maximize deterministic control.
The Architectural Blueprint: The Adapter Pattern
Think of an interface agent as an implementation of the classic Adapter Pattern in software engineering. Rather than letting Agent A blast a wall of unstructured text at Agent B, the Interface Agent sits between them as a protocol translator.
[Agent A: Creative Planner]
│ (Unstructured Reasoning/Text)
▼
┌───────────────────────────────────────┐
│ INTERFACE AGENT (The Adapter) │
│ 1. Low-latency LLM parses text │
│ 2. Forces output into Pydantic schema│
└───────────────────────────────────────┘
│ (Strict, Validated JSON Payload)
▼
[Deterministic Validator / Guardrail] ──> (Fails? Halt / Alert Human)
│ (Passed)
▼
[Agent B: Deep Technical Auditor]
3 Core Responsibilities of an Interface Agent
To keep your framework highly stable, restrict your interface agents to three specific technical functions:
1. Structural Schema Enforcement (The Protocol Guard)
The interface agent takes the chaotic, conversational output of a high-creativity reasoning model (running at a higher temperature) and maps it into a precise data structure.
The Implementation: You can utilize a lightweight, fast, local model (like a highly fine-tuned 7B model or an ultra-low temperature inference call) running structured outputs (JSON mode) to extract exactly what the next agent needs.
2. Context Pruning and Summarization (The Window Cleaner)
Downstream agents will degrade in performance if you dump thousands of tokens of upstream conversational history into their prompt context windows.
The Implementation: The interface agent acts as an information filter. It strips out the "conversational fluff," pleasantries, and intermediate chain-of-thought tokens from Agent A, distilling the output down to the raw metrics, requirements, or core vectors required by Agent B. This preserves downstream context and slashes token latency.
3. Impedance Matching (The Domain Translator)
Different agents in your team will have completely different systemic goals, system prompts, and toolsets.
The Implementation: If Agent A is a high-level strategic planner and Agent B is an expert code execution sandbox agent, they don't speak the same language. The interface agent translates intent into specification, ensuring Agent B receives clean, parseable inputs.
When to Use an Interface Agent vs. Pure Code
To avoid over-engineering your framework, use this simple rule of thumb to decide if an interface requires an AI agent or a standard Python function:
Use Pure Python Code if: Upstream Agent A can already output structured JSON (using Pydantic/Instructor). If the output is already structured, use deterministic data parsing, dictionary mapping, or string manipulation to pass the data to Agent B. Do not inject an LLM where deterministic code can do the job.
Use an Interface Agent if: Upstream Agent A must remain entirely unconstrained and creative (e.g., brainstorming architectural variations, analyzing ambiguous source text), meaning its output format cannot be safely guaranteed by a rigid schema. The interface agent is deployed specifically to clean up that creative wake before it hits the next phase of your pipeline.
Implementation Example: The Schema Enforcement Gate
In your framework's codebase, your execution loop can invoke an interface agent dynamically whenever an unstructured boundary is crossed:
from pydantic import BaseModel, Field
from typing import List
# 1. Define the rigid contract Agent B expects
class TechnicalSpecification(BaseModel):
component_name: str
security_clearance_required: bool
input_parameters: List[str]
max_timeout_seconds: int = Field(default=30)
# 2. The Interface Agent execution block
def run_interface_agent(raw_upstream_output: str) -> TechnicalSpecification:
# Use your local LLM client with a strict JSON system prompt
# and temperature set to 0.0
structured_json = local_llm_json_call(
system_prompt="Extract the technical specifications from the text into the requested schema.",
user_content=raw_upstream_output,
response_model=TechnicalSpecification # Enforced by your framework
)
return structured_json
By constraining your interface agents to generating strict schemas rather than conversational text, you create a system that can be automatically unit-tested and validated at every handoff. If the interface agent fails to output a schema that satisfies your Pydantic model, your core state machine catches the validation error instantly and halts execution before any damage can occur downstream.
Are you thinking of using these interface agents primarily to translate data structures between completely different model families (e.g., passing data from a massive cloud-based reasoning model to a small, fast local model), or are they managing handoffs within the same local ecosystem?
From <https://gemini.google.com/app/> Google Gemini (3.5 Flash)
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