Infrastructure for building software with autonomous AI agents. A set of integrated architectural domains that make AI-generated code auditable, governed, observable, and evidence-backed.
The platform turns AI agents into accountable workers rather than one-shot code generators. It wraps agent execution with the same controls a regulated engineering organization applies to human developers: versioned identity, scoped permissions, peer review, audit trails, cost tracking, reproducibility, and documentation.
When an agent writes code, the system records who authorized the work, what the agent was allowed to do, what it actually did, which independent validator reviewed the output, and what it cost. All of this happens automatically in the background while the agent operates.
Status: These are working systems the author runs, published as 25 architecture PRDs alongside their source repositories. They are individual components you deploy and operate yourself — not a hosted product and not a single-command install. The repositories are being prepared for public release; links to them resolve once each repo is made public.
An LLM produces 200 lines of code. Three weeks later a defect ships to production. Nobody knows what prompt produced it, which model version, what context was in memory, or what the agent was instructed to do. The platform captures all of this as structured, queryable records.
Autonomous agents can escalate privileges, exfiltrate data, ignore scope boundaries, and make decisions they shouldn't. Identity manifests, tiered tool classification, and runtime policy enforcement constrain what each agent can do based on trust level and data classification.
Every conversation starts from scratch. Knowledge, decisions, and successful patterns from prior work are lost. The persistent vector memory system retains semantic knowledge across sessions, with brain-inspired consolidation, hot/warm/cold tiering, and cross-project context transfer.
Agents that grade their own homework claim success regardless of actual output quality. Agent runs are independently validated by a different model (Gemini validates Claude output) with structured PASS/FAIL verdicts and finding-level remediation loops.
Token usage compounds invisibly. A runaway agent loop can consume thousands of dollars before anyone notices. Per-interaction cost metering, a four-level budget hierarchy (organization / project / agent class / agent instance) with warn, throttle and pause enforcement, and a cost-per-outcome metric link spend to delivered work.
Frameworks that govern AI systems require human attribution, tamper-evident audit trails, model governance documentation, and reproducible evidence. The compliance service scores live governance events against ISO 42001, the EU AI Act, OWASP Agentic, SOC 2, ISO 27001 and GLBA control domains, and assembles signed evidence packages from system state rather than manual reconstruction.
Senior engineers inspect AI output before merge — but inconsistently, under time pressure, and with no systematic record. Code Hardener integrates 27 open-source security tools powering 37 named analyzers, grouped into scan profiles, and produces cryptographically signed quality reports.
A pipeline runs for two hours, fails on step 9 of 12, and restarts from scratch — losing the successful work from steps 1–8. Self-healing workflow recovery classifies failures into 7 categories, resumes from checkpoints, and applies category-appropriate remediation (retry, reroute, degrade, escalate).
Different vendors speak different protocols, and agents can't discover or delegate to each other across systems. The A2A gateway publishes an explicit allowlist of agents over REST, an MCP bridge, and the Google A2A protocol, with capability discovery at /.well-known/agent.json. A pluggable invoker template lets it drive whatever agent runtime you already run.
| Component | Role |
|---|---|
| Plugin ecosystem | Declarative extension model with two-layer hook architecture — drop files in a directory, they become system behavior |
| Multi-agent orchestration | 37 specialized agents (18 workflow + 19 kernel) coordinated through tiered quality gates (TRIVIAL / MINOR / STANDARD / MAJOR) with independent Gemini validation |
| Persistent vector memory | 74 MCP tools, Qdrant vector database, 34 scheduled n8n workflows for consolidation and pruning, brain-inspired memory architecture |
| Knowledge graph (GraphRAG) | Memgraph-backed relational memory with temporal edges — answer "what changed, when, and why" |
| Agent governance | Identity manifests with trust levels 1–5, append-only audit bus, runtime policy enforcement via pre-tool hooks |
| Code assurance | 27 integrated open-source tools powering 37 analyzers (22 quality + 15 security), with mutation testing, Ed25519 attestation, SLSA provenance and 1000-point quality scoring |
| Agentic data plane | DAG-based lineage with chain verification, dataset quality validation and enforcement, pipeline observability, and PII/PHI classification |
| Agent economics | Per-interaction cost metering, model routing, budget hierarchy, semantic caching, cost-per-outcome reporting |
| Regulatory compliance | Cryptographically-chained immutable audit, versioned evidence packages, human decision gates with signed receipts, data-subject-request routing, incident tracking |
| A2A interoperability | Allowlisted agents exposed via REST, MCP bridge, and the Google A2A protocol, with Agent Cards at /.well-known/agent.json |
These are the properties the architecture is built to provide when the components are deployed and wired together:
Organizations subject to AI and information-security frameworks — ISO 42001, the EU AI Act, SOC 2, ISO 27001, GLBA — that need to show AI agent operations are governed and evidenced. The compliance layer scores live governance events against those control domains and packages the results.
Teams who have moved past AI-assisted autocomplete to autonomous agentic workflows (planner + builder + reviewer + tester) and need accountability, cost control, and systematic quality gates.
Teams building internal developer platforms that incorporate AI agents and need governance, observability, and multi-tenant budget controls that map to organizational hierarchy.
CISOs, compliance officers, and internal auditors who need to demonstrate that AI agent operations are governed, auditable, and aligned with frameworks the organization is subject to.
The domains are not a loose collection of features — they compose into a coherent execution stack:
At session start: The compliance layer anchors the session to a human identity and responsible party. Context guard tracks how close the session is to its compaction boundary, memory auto-recall pulls relevant prior work, and identity manifests establish what each agent can do.
During work: The conductor orchestrates 37 agents through tiered workflows. Agent dispatches are validated by Gemini. Tool calls are governance-checked. Costs are metered. Outputs are recorded in the immutable audit chain with cryptographic ordering. Stigmergy traces let agents coordinate without constant conductor relay.
At checkpoint: Code Hardener runs its analyzer suite against generated code. Failing findings dispatch targeted fix agents. Adversarial review has Claude and Gemini debate disputed findings. Data-plane quality gates enforce dataset thresholds before results propagate.
At release: The evidence package generator produces a signed, versioned bundle containing the audit trail, gate decisions and artifacts, with an Ed25519 signature and a verification endpoint.
Continuously: 34 n8n workflows consolidate memory, prune stale data, detect contradictions, run red-team scans, and generate weekly digests. The outcome collector passively measures 8 metrics including cost per outcome. Predictive scaling analyzes trajectories to optimize model routing and cache warming.
Next step: Read the architecture PRDs for technical depth on any domain, or the documentation for how-to guides on using each capability.