Platform documentation
Eight integrated capability layers, the Autonomous Agent, knowledge base, Hub, voice, vision, code, dialogue, observability and workspace management.
Updated: September 2026A · Orchestration & Execution Intelligence
The engine layer. Runs complex work end-to-end; routes requests intelligently and keeps multi-step processes stable even when dependencies fail. Every run is stateful: steps, intermediate results and decisions are recorded.
- Supervisor → sub-agent → supervisor architecture
- Dynamic agent routing
- Stateful multi-step execution; parallel branches and loops
- Agent-to-agent communication (A2A)
- Scheduled and webhook triggers
- Agent templates and the visual agent builder
- Autonomous Agent: build agents by dialogue with test corpora
B · AI / Model Orchestration
Brain selection and resilience. Uses the right model for each task; reduces vendor lock-in and keeps operations running when a provider is unavailable. Switching models does not require redesigning the agent.
- Multi-model gateway: proprietary and open-source models behind one interface
- Model proxy: observable, masked access to cloud models with your own keys
- Model switching without redesign
- Fallback model strategy
- On-prem serving for open-source models
- Prompt testing environment and versioning
- Hub Aginies: enterprise fine-tuned models (c5, g5, g4, oss) and speech models
C · Knowledge & Retrieval
Enterprise memory. Turns fragmented enterprise information into governed, searchable, trustworthy knowledge; answers link back to sources. Chunk-level control makes RAG quality directly manageable.
- Document embedding pipeline
- Versioned knowledge base management
- Tag-based document segmentation
- Hybrid search and multi-source RAG
- Chunk-level inspection and editing
- Hallucination-reduction architecture
- Source, tag and access-policy management
D · Governance, Risk & Compliance
The control layer. Makes AI auditable, safe and fit for regulated environments; every decision is traceable. Governance is not bolted on; it is part of the architecture.
- Guardrails and policy engine
- Human-in-the-loop checkpoints: threshold and approver based
- Agent decision traceability
- Sensitive data masking and PII redaction
- Masking and anonymisation of all requests
- Full input/output logging and audit trail
- Kill-switch and automation limits
E · Observability & Optimisation
Performance management. Provides visibility into accuracy, speed, cost and health; produces the data for continuous improvement. Every run is recorded step by step.
- Per-run traces and timelines
- Latency and token tracking
- Cost attribution by scenario, unit and channel
- Usage and cost per model; platform health
- Routing accuracy measurement
- Dashboard and analytics layer
- Live logs, conversation records, per-agent success heatmap
F · Integration & Connectivity
Enterprise reach. Connects to enterprise systems and customer channels without rebuilding the existing technology landscape. Each tool carries its own invocation policy.
- REST API and database integrations
- Model Context Protocol (MCP) servers
- Custom tool definitions and invocation policies
- Native connectors: email, calendar, files, collaboration tools
- Web widget and messaging integrations
- MCP Hub: catalogue of approved tool servers
G · Multichannel Experience & Voice
The customer interaction layer. Delivers AI across digital, messaging and voice channels on one consistent orchestration underneath. The same agent runs in text and voice.
- Real-time speech-to-text and streaming voice processing
- Call-centre SIP integration
- WhatsApp, SMS and email services
- Intent detection and entity extraction blocks
- Session memory
- Dialog Aginies: dialogue UI and human takeover
- Voice Aginies: Turkish STT, TTS and ASR models
H · Deployment & Enterprise Architecture
Fit to enterprise constraints. Supports the on-premise, hybrid and sovereignty requirements of large enterprises. Data stays where it must.
- On-premise and air-gapped deployment
- Hybrid architecture: sensitive workloads inside, elastic capacity outside
- Private cloud / dedicated region
- SSO / SAML, fine-grained RBAC, workspace isolation
- Per-tenant agent sharing and collaborative development
- GPU-as-a-Service for in-country elastic capacity
Autonomous Agent
The Autonomous Agent builds agents by dialogue. You describe what you want; it sets up the supervisor and specialist agents, wires tools and the knowledge base, chooses models, defines approval points, writes and runs a test corpus, and deploys after your approval. Every agent in the library can be produced this way.
- Describe: objective, channels and approval rule are given in natural language.
- Build: supervisor and specialist agents are created; a version number is assigned.
- Wire: tools (REST/MCP) and the knowledge base are attached; chunk counts are reported.
- Model selection: separate models for intent, decisions and speech; on-prem preferences respected.
- Approval points: threshold and approver defined (e.g. amount > 100,000 → branch manager).
- A test corpus is written and run; check families are scored.
- Human approval: results are presented; the deployment decision always stays with a person.
- Deploy: the new version goes live; logs and dashboard are active immediately.
A test corpus has three levels: suites (golden path, branches, robustness injection), scenarios within each suite, and multi-turn conversations within each scenario. Every turn is scored against check families.
- customer_resolved: was the customer’s request actually resolved
- pii_before_verification: was personal data disclosed before identity verification
- State machine: did the conversation stay within defined states and transitions
- Memory round-trip: was information from earlier turns recalled correctly
- Judge dimensions: qualitative criteria such as accuracy, tone and policy adherence
- Tool and approval checks: right tool, right parameters, approval triggered above threshold
Coverage shows what share of defined intents, branches and tools the corpus exercises. Low coverage is surfaced as a warning before deployment; results are kept per version and compared across versions.
Knowledge Base
The knowledge base lets agents answer with sources. Documents pass through the embedding pipeline, are split into chunks, tagged and versioned. Each agent knows which sources it may reach, with which tags and under which access policy.
- Sources: files, folders, web, databases and connectors
- Chunk editor: inspect, edit or disable any chunk
- Versioning: which agent ran with which knowledge base version is recorded
- Hybrid search: semantic and keyword retrieval combined
- Access policy: visibility by tag and role
- Citation: every claim in an answer links to its chunk
Hub Aginies: Model Hub, MCP Hub and proxy
The model catalogue holds three families in one place: enterprise fine-tuned versions of proprietary models (c5, g5, g4), open-source-based on-prem models (oss), and Turkish speech and vision models. Each scenario gets the model that balances accuracy, latency and cost.
| Model | Base | Where it runs | Typical use |
|---|---|---|---|
| aginies-large-c5 | Proprietary, enterprise fine-tuned | Cloud, via gateway with masking | Complex decisions and multi-step reasoning |
| aginies-medium-c5 | Proprietary, enterprise fine-tuned | Cloud, via gateway with masking | General dialogue, summarisation, extraction |
| aginies-large-g5 | Proprietary, enterprise fine-tuned | Cloud, via gateway with masking | Long-document analysis and research |
| aginies-large-g4 | Proprietary, enterprise fine-tuned | Cloud, via gateway with masking | High-volume production tasks |
| aginies-medium-g4 | Proprietary, enterprise fine-tuned | Cloud, via gateway with masking | Cost-sensitive classification and routing |
| aginies-medium-oss-120b | Open-source based | On-prem | In-house reasoning for sensitive workloads |
| aginies-small-oss-32b | Open-source based | On-prem | Intent detection, routing, fast classification |
| aginies-small-oss-27b | Open-source based | On-prem | Lightweight checks, guardrails and pre-processing |
| aginies-vision-oss-72b | Open-source based vision model | On-prem | Document and image understanding, masking |
| aginies-voice-tr-v3 | STT trained for Turkish | On-prem | Speech-to-text, call transcription |
| aginies-tts-tr-v2 | TTS trained for Turkish | On-prem | Natural speech synthesis, voice agent replies |
| aginies-asr-stream-v1 | Streaming ASR | On-prem | Real-time call-centre recognition |
Model proxy: you enter your own cloud keys and the platform reaches cloud models on your behalf as a proxy. Every call passes through one point, is observed and masked. Keys are stored encrypted; usage and cost are reported per agent, unit and channel.
- Switch models with one selection; the fallback strategy takes over during provider outages
- Prompt testing environment and versioning; comparison across versions
- MCP Hub: catalogue of approved tool servers; access and invocation policy per server
Voice Aginies
Speech models trained for Turkish: streaming speech-to-text (STT), natural text-to-speech (TTS) and real-time ASR. Voice agents connect through call-centre SIP integration and run on the same orchestration as text-channel agents.
- Models: aginies-voice-tr-v3 (STT), aginies-tts-tr-v2, aginies-asr-stream-v1
- On-prem serving; audio never leaves the enterprise
- Voice analysis: quality scoring, sentiment and intent
- 100% of calls transcribed and scored against the quality form
- Personal data is masked during transcription
Vision Aginies
Image and document understanding: invoice, contract and form extraction, damage-photo assessment, visual quality control. Personal-data detection and document masking run on the same models, inside the enterprise.
- aginies-vision-oss-72b · on-prem
- Field extraction with source document and confidence score per field
- Document classification, independent of layout
- Personal-data detection and document masking (KVKK/GDPR)
- Low-confidence fields route to human verification
Code Aginies
For engineering teams: IDE extensions and in-house code completion, an enterprise IDE registry that distributes approved extensions and models, and the SDLC agent team working in the CI/CD pipeline. Code never leaves the enterprise.
- In-house code completion on on-prem models
- IDE Registry: approved extension and model distribution, version control
- SDLC agent team: PM, analyst, architect, developer, tester
- Works with Jira, Git and CI; code review and the production decision stay with people
Dialog Aginies
The dialogue surface for customers and employees: web widget, mobile, WhatsApp and messaging. Session memory, human takeover and an audit trail for every message. The same agent runs in text and voice channels.
- Human takeover and hand-back; context handed to the representative with a summary
- Conversation dashboard and quality scoring
- Intent detection and entity extraction blocks
- Integration with the identity flow; unauthorised requests stop at the guardrail
Observability and reporting
What happens per agent, how the platform is doing and what each model costs: on one dashboard. Every run is recorded step by step; logs and conversation records are reviewed under role-based access.
- Live runs: step, duration, tokens, target system and outcome
- Per-agent success heatmap and error timeline
- Quality and error timelines; routing accuracy
- Cost attribution by scenario, unit, channel and branch
- Usage and cost per model; platform health
- Capacity projection: cost and resource forecast with usage growth
- Audit reporting: input, output, model version, rule set, user
Reports can be exported and retained in a form suitable for internal audit and regulatory requests. Conversation records are stored masked; unmasked access requires separate authorisation.
Workspaces, tenants and collaboration
The platform is separated by workspace and tenant. Each team manages its own agents, knowledge bases and keys in isolation; approved agents are shared inside the organisation and developed together.
- Identity through SSO / SAML; permissions through fine-grained RBAC
- Workspace isolation: data, logs and cost reported separately
- Per-tenant agent sharing and collaborative development
- Templates, users and licences under central management
- Agent versions and release history recorded per workspace
Product screens
| report | breakdown | schedule | format |
|---|---|---|---|
| Monthly cost report | scenario · unit · channel · branch | 1st of month · 07:00 | PDF · XLSX |
| Agent performance | success, latency, error classes | weekly · Monday | |
| Audit export | run traces, approvals, model versions | on demand | CSV · JSON |
| Model usage | tokens, cost and proxy share per model | daily · 06:00 | XLSX |
| SLA and capacity | p95 latency, queues, GPU occupancy, projection | weekly |
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| model | access | usage · 7 days | cost |
|---|---|---|---|
| aginies-large-c5 | proxy · your key | 62% | $412 |
| aginies-large-g5 | proxy · your key | 38% | $288 |
| aginies-medium-oss-120b | on-prem · GPU 2× | 84% | — |
| aginies-small-oss-32b | on-prem · GPU 1× | 91% | — |
| aginies-voice-tr-v3 | on-prem · STT | 47% | — |
| aginies-vision-oss-72b | on-prem · vision | 22% | — |
| agent | role · team | hour-eq. | output | quality | mode |
|---|---|---|---|---|---|
| ios-dev-01 | iOS Developer · mobile team | 14 h | 9 PRs · 21 points | 4.7 | autonomous · 10% review |
| qa-01 | QA Engineer · mobile team | 31 h | 412 tests · 6 bugs | 4.8 | autonomous · 10% review |
| ba-01 | Business Analyst · payments product | 9 h | 11 stories · 2 processes | 4.5 | shadow · 100% review |
| acct-01 | Accountant · finance | 27 h | 238 entries · 3 reconciliations | 4.9 | autonomous · threshold-approved |