In-depth architecture analyses, agentic pipeline designs, and production lessons from building custom enterprise software and modern AI infrastructure.
Silent regressions in AI-generated code break production. We built a self-healing pipeline driven by Reinforcement Learning from Compiler Feedback and MCTS.
Running agents across enterprise tenants requires strict isolation. Here is our multi-tenant agent mesh with sub-10ms WebAssembly state restoration.
Single-agent refactoring stalls on multi-file boundaries. We engineered a sub-agent swarm with speculative parallel execution and dynamic context slicing.
Rewriting legacy monoliths manually takes years. We engineered a speculative multi-agent translation pipeline that converted legacy services in 3 weeks.
Static code reviews miss complex authorization leaks. Here is how we engineered an AST Mutation and Security Guardrail Engine for agent-generated code.
Agents fail on multi-hour tasks due to context entropy. Here is how we engineered an Epistemic Context Compactor for 100+ step autonomous execution.
Sequential agent chains create painful 20-second delays. We re-architected multi-agent execution into an event-driven async DAG with real-time streaming.
Traditional unit tests fail on non-deterministic LLM software. Here is how we build automated CI/CD evaluation suites with calibrated LLM judges.
Connecting hundreds of tools into LLMs causes token bloat and selection errors. Here is our two-tier MCP proxy gateway with dynamic tool indexing.
Vector search alone misses relational context. Here is how we combined pgvector and Neo4j into a sub-100ms GraphRAG architecture for enterprise data.
Relying on LLMs without strict specs leads to non-deterministic failure. Spec-driven engineering uses typed schemas and state machines to enforce safety.
Prompt engineering got us started, but scaling fast agents requires context engineering. Here is how we cut latency and costs by 85% with prompt caching.
Chat boxes are an inefficient interface for work. The true ROI of AI lies in autonomous agents executing multi-step business workflows behind the scenes.
Generic AI SaaS wrappers promise instant integration but trap your data in rigid silos. Here is why building custom AI software creates a defensible moat.
Tutorial RAG pipelines work fine on clean PDFs, but collapse in production. Here is how to handle complex tables, hybrid search, and latency at scale.
How we migrated a 1.2M-line legacy financial monolith to event-driven Go microservices in 12 weeks with zero downtime using Tree-sitter AST swarms.
Docker containers take 800ms to spin up—far too slow for interactive coding agents. Here is our sub-10ms WebAssembly and eBPF micro-sandbox architecture.
Single-pass LLMs plateau on architectural decisions. We built an inference-time compute engine that scales search depth dynamically based on task difficulty.
IDE auto-completion demands sub-50ms latency. Here is how we engineered an on-premise code engine using speculative decoding and model distillation.
Unstructured agent communication causes deadlocks. We built a DAG-based orchestration engine with state reconciliation for 100% deterministic swarms.
1M-token contexts are slow and expensive if unmanaged. We engineered prompt caching breakpoints and dynamic KV compaction to cut costs and latency by 85%.
Dense vector search alone cannot navigate code hierarchies. We unified AST call-graphs with vector similarity for sub-100ms multi-hop codebase retrieval.
Dumping 500+ MCP tools into agent prompts kills context budgets. We engineered an MCP Gateway with distributed tool discovery and sub-10ms routing.
Scaling parameters is plateauing; scaling inference compute is where real gains happen. Here is how we implemented MCTS execution trees for coding agents.
Direct LLM API calls are slow and pricey. We built an enterprise AI gateway with sub-20ms semantic caching and speculative multi-model routing.
Flat chain-of-thought tokens get lost during multi-hour refactors. Here is how we built stateful memory scratchpads and recursive reasoning trees.
Human feedback is too slow for complex code training. We built an RLVR pipeline using Group Relative Policy Optimization (GRPO) for self-improving agents.
Running agent-generated code directly on host machines invites disaster. Here is our least-privilege sandbox with eBPF and AST security gateways.
A single agent refactoring complex repositories makes reckless edits. We split the job into Initializer, Coder, and Evaluator loops for 98.6% clean merges.
Long-running agents bloat their context windows with useless logs. We built an epistemic context compactor that saves 82% of tokens with zero memory loss.
Prompts change and agents silently regress. Here is how we built a continuous CI/CD evaluation harness with calibrated LLM judges and unit gates.
Outcome rewards only tell you if the final test passed. We built a Process Reward Model that evaluates intermediate AST steps to catch bugs early.
Monolithic agent loops drift and get stuck in rabbit holes. Here is how we engineered deterministic Orchestrator-Worker state machines with MCP.
Giving agents access to SQL and APIs without containment creates severe security holes. Here is our dual-pass taint tracking and guardrail engine.
Manual monolith migrations take years. We migrated a 2M-line legacy Java banking monolith to event-driven Go services using AST translation swarms.
Sluggish LLM inference kills developer adoption. We achieved 3.2x faster agent token decoding using speculative draft models and paged KV caches.
Single-pass LLMs struggle on complex refactors. Here is how we implemented Monte Carlo Tree Search and verification harnesses for coding agents.
Stuffing hundreds of tool schemas into context windows crashes LLM reasoning. Here is our sub-10ms prefix-cached router for enterprise MCP.
Raw screenshot agents are slow and fragile. We combined Accessibility tree parsing, frame diffing, and sandboxing for sub-50ms GUI automation.
Cascading speech-to-text pipelines ruin conversation flow. Here is how we engineered a sub-120ms voice agent with frame-level barge-in handling.
Multi-agent coding swarms fail when hallucinations compound. We built a BFT consensus engine with speculative AST rollouts for 99.2% accuracy.
Standard KV caches collapse under million-token workloads. Here is how we combined Mamba-2 SSMs and linear attention for sub-millisecond throughput.
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