AptAgents · Coming Soon · Platform Brief v2026

Self-improving agents your team can put into production.

An end-to-end platform for reliable and secure agentic AI. Twelve composable pillars across reliability, safety, governance, observability, evaluation, prompt optimization, fine-tuning, red-teaming, knowledge, memory, tools, and orchestration.

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Three theses behind the platform

Why AptAgents — in three claims.

Reliability through communication theory.

AgentCodec carries six primitives from communication theory into the LLM layer: diversity ensembling, hybrid ARQ, turbo decoding, rateless fountain codes, forward error correction, and adaptive coding and modulation. They generalize self-consistency, self-refine, and chain-of-verification — and outperform them on benchmark. SNR maps to task difficulty. Diversity order sets the slope of the failure-rate curve. Hallucination becomes a quantity you can budget against.

Pipeline-level safety and governance.

The AgentFirewall wraps every workflow in three phases. Intent screening blocks policy-violating requests in ~400ms. PII detection and structured-output enforcement run during execution. Output scanning catches policy violations on the way out. Behind the firewall, an ABAC engine evaluates every access decision and writes the result into a SHA-256 hash-chained audit log no endpoint can mutate.

Closed-loop self-improvement.

Three pillars compose into a continuous improvement cycle. AgentPrompts searches the prompt space with DSPy MIPROv2, OPRO, APE, and TextGrad. AgentAlign fine-tunes the runs that worked with SFT, DPO, IPO, ORPO, or KTO on RunPod GPUs. AgentSim red-teams what shipped through automated jailbreak campaigns. Each stage produces inputs for the next.

Overview

The twelve pillars.

Each pillar ships as an independently installable Python module with its own library API, CLI, configs, and benchmark harness. Install only the ones you need.

01 / 12

AgentCodec

Reliability Science

Six comm-theory primitives: diversity, HARQ, turbo, fountain, FEC, ACM. Generalizes self-consistency, self-refine, CoVe.

02 / 12

AgentGuard

Runtime Safety

Three-phase AgentFirewall: intent → execution → output. Prompt-injection defense, sandbox, structured outputs, HITL.

03 / 12

AgentGovern

Policy, Audit, Compliance

ABAC engine, classified assets, subject grants. SHA-256 hash-chained audit; SOC 2 / HIPAA / GDPR reports.

04 / 12

AgentObserve

Tracing & Telemetry

LangFuse integration; per-call latency, tokens, cost. Anomaly detection with severity-tiered alerts.

05 / 12

AgentMetrics

Eval, A/B, Statistical Rigor

Datasets, preference pairs, custom evaluators, LLM-as-judge. A/B with paired t-tests + bootstrap CIs.

06 / 12

AgentKnowledge

Hybrid RAG

BM25 + dense-vector retrieval, pluggable embeddings. Configurable chunking, citations, optional Cohere reranking.

07 / 12

AgentMemory

Working / Episodic / Semantic

Four memory tiers with importance scoring and consolidation. MemGPT-style virtual context; per-user/session scoping.

08 / 12

AgentTools

Tool Registry + MCP

Tavily, E2B, custom REST tools, dynamic selection. MCP: consume external servers, expose your own.

09 / 12

AgentPrompts

Prompt Optimization & Registry

Automatic prompt search via DSPy MIPROv2, OPRO, APE, and TextGrad. Versioned registry with one-click promote-winner.

10 / 12

AgentAlign

Fine-Tuning & Preference Learning

SFT, DPO, IPO, ORPO, KTO via PEFT + LoRA / QLoRA. RunPod GPU dispatch; trained-model registry.

11 / 12

AgentSim

Adversarial Red-Team

Automated jailbreak generation and attack-success benchmarks. Continuous red-teaming wired into CI.

12 / 12

AgentOrchestrate

Workflow Kernel & Planning

Multi-agent coordination, DAG executor, visual canvas. ReAct, plan-and-execute, hierarchical, Tree-of-Thought.

Pillar 01 · AgentCodec

Communication-theoretic reliability for LLM agents.

Six primitives that generalize self-consistency, self-refine, and chain-of-verification.

MIMO-style synthesis

Diversity Ensemble

N parallel branches combined via MRC, SC, or EGC. Diversity gain shrinks hallucination probability by order N.

Retry with information

HARQ (Hybrid ARQ)

Retries until quality clears the threshold. HARQ-IR adds new hints; HARQ-CC soft-combines all attempts.

Iterative SISO

Turbo Decoder

Generator drafts. Critic returns structured extrinsic info. Re-drafts until cosine-sim convergence.

Adaptive sampling

Fountain / Rateless

Estimates channel capacity as pairwise similarity; stops at confidence threshold.

Systematic block code

Forward Error Correction

PRIMARY_ANSWER plus parity (REASONING, VERIFICATION, ALTERNATIVES, EDGE_CASES). Decoder cross-checks for consistency.

Adaptive coding & mod

ACM Router

Routes by estimated task complexity. Four zones: easy → haiku, moderate → sonnet, hard → opus, very hard → opus + ensemble.

Pillars 02 + 03 · AgentGuard + AgentGovern

A three-phase firewall wraps every execution.

Every workflow flows through three phases of defense.

Phase 01

Intent Pre-Check

Permissions pre-fetched async, in parallel with the LLM call.
LLM Guard NER scan on input (~5 ms).
Claude Haiku semantic intent analysis (~400 ms).
Blocks policy-violating intent before any cost is spent.

Phase 02

Execution

Workflow runs through the WorkflowEngine DAG.
May run speculatively in parallel with Phase 1 for read-only flows.
Per-node guardrails enforce policy at every LLM call.
AgentGuard wraps any node with governance enabled.

Phase 03

Response Scan

LLM Guard NER scan on the response (concurrent).
Claude Haiku semantic scan for unauthorized content.
Blocks or redacts if disallowed data reaches the output.
Every decision written to the audit chain.

Pillars 09–11 · AgentPrompts + AgentAlign + AgentSim

The self-improvement loop.

Optimize the prompt. Fine-tune the model. Red-team the result.

Stage 01

AgentPrompts

DSPy MIPROv2 · OPRO · APE · TextGrad
Bayesian search over prompt hyperparameters
Versioned registry · one-click promote-winner

Stage 02

AgentAlign

SFT · DPO · IPO · ORPO · KTO
LoRA / QLoRA via PEFT
RunPod GPU dispatch · trained-model registry

Stage 03

AgentSim

Automated jailbreak generation
Attack-success benchmarks per model & version
CI hook · block merges that regress on safety

Infrastructure

Any provider. Any model. Zero exposed ports.

AptAgents is provider-agnostic by design. Run frontier models, open-source models, or models you trained yesterday on your own GPUs.

Deploy to production in 3 commands

cp .env.prod.example .env.prod
# DOMAIN, CLOUDFLARE_TUNNEL_TOKEN, passwords, API keys
docker compose -f docker-compose.prod.yml up -d

Cloudflare Tunnels

Zero exposed ports. cloudflared makes outbound-only connections. SSL/TLS, DDoS protection, WAF, and CDN included.

Gunicorn + UvicornWorker

Multi-process ASGI with 2×CPU+1 workers and a 120-second timeout for long LLM calls.

Caddy (Frontend)

Serves the SPA build with gzip and zstd compression.

Redis

slowapi rate limiting at 300 req/min by default. arq async task queue and 512 MB LRU cache share the instance.

PostgreSQL 16

Primary store on asyncpg, tuned for production. pgvector for embeddings; separate LangFuse database for traces.

Docker Compose

All services on an internal bridge network with nothing exposed to the internet.

Use cases

Built for teams that own outcomes.

AptAgents is purpose-built for production teams whose AI has to be right, auditable, and improving from one release to the next.

Mission-Critical AI

Where outputs must be measurably reliable: regulated industries, decision support, agentic automation with real-world consequences.

Regulated & Auditable

Full trace per run, HITL approvals, ABAC enforcement, immutable audit chain. SOC 2, HIPAA, GDPR controls.

Document Processing

Read, classify, extract structured data from PDFs at scale. Combine RAG with FEC reliability for verifiable accuracy.

Code Generation

LLM-powered code-gen with Turbo-decoding reliability and E2B sandbox execution. A/B test prompts to maximize pass rates.

Adversarial / Red-Team

Continuous attack-success benchmarks via AgentSim. Wire jailbreak campaigns into CI; block regressions on safety.

Multi-Step Reasoning

Map/Reduce parallelism, conditional branching, sub-workflows, ACM Router for cost-optimal model selection per step.

Comparison

How AptAgents compares.

Native, partial, or not-native capability as of 2026.

Capability	AptAgents	LangChain / LangSmith	W&B Weave	Humanloop	OpenAI Assistants
Comm-theory reliability (AgentCodec)
Three-phase AgentFirewall + ABAC + audit
Adversarial red-team (AgentSim)
Prompt optimization (DSPy + Bayesian)
A/B with statistical significance
GPU fine-tuning (SFT/DPO/IPO/ORPO/KTO)
SOC 2 / HIPAA / GDPR reports
HITL approvals + email notification
Four-tier agent memory
MCP (Model Context Protocol)
Self-hostable, zero exposed ports
Visual workflow canvas

native partial not native

Platform at a glance

Numbers worth remembering.

Pillars

Reliability primitives

Fine-tune methods

Optimization methods

Memory tiers

Workspace roles

Compliance reports

∞

Provider integrations

Want AptAgents inside your perimeter?

Self-hostable. Production-ready with Docker Compose in minutes. We'll guide you through the rollout — from architecture review to first live workflow.

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