Five analysts fan out in parallel within one superstep, so latency is max rather than sum; backtesting and reflection stand before synthesis, restricting the LLM to explaining evidence that already exists.
Only two roles call an LLM; every other analyst remains fully programmatic. Each call follows an Anthropic API → local Claude CLI → rules-based degradation chain, and cost accounting trusts only provider-reported values—unknown cost is never treated as $0.
This project has one core rule: every LLM conclusion must first pass a historical backtest of the same signals. When expectancy is negative, synthesis cannot issue an optimistic verdict. Each of the four traps that make backtests lie has a programmatic countermeasure.
I do not measure whether the agent ‘feels accurate.’ I freeze parameters in walk-forward OOS tests, record a hash of every input in run cards, and keep the honest 5/10 = 50% golden-eval baseline so the agent has to admit that it is not accurate yet.
Numbers are the easiest part of an LLM report to hallucinate. I therefore put every trusted number into a SHA-256-addressed evidence manifest and let the LLM cite only {{fact.id}} placeholders. If it writes a bare number, the entire output is discarded and replaced with a deterministic template.
A research request first becomes a ResearchPlan that requires human approval. External documents must be fetched in full, and verbatim quotes must be verified before they can enter a report. Quant review is always append-only, and free-text feedback never flows back into a prompt. This is the complete M5 Copilot loop.
Three frozen Pydantic contracts weld the boundary between a research artifact and order-placement authority shut: content addressing, eight hard gates, and paper-only execution, while the agent never touches credentials.
The full deployment path for a Python agent, from local uv run to Docker to a public API on Cloudflare Containers: the Worker enforces authentication, the Container runs FastAPI, secrets never enter the image, and the service sleeps automatically after 10 idle minutes—the right way for a side project to save money.
Ably manages global realtime connections, channels, presence, and short-window recovery; applications still own idempotency, durable business state, token capabilities, and offline resynchronization.
An agent should execute one task with a short-lived, audience- and permission-restricted credential while preserving user and agent identities, execution-time authorization, confirmation, and audit lineage.
Agent execution must constrain kernels, filesystems, processes, networks, credentials, and tool authorization; sandbox escape is only one path, and an overpowered API token is often more direct.
Linode is now the compute foundation of Akamai Cloud Computing; VMs, LKE, storage, and databases retain regional and network boundaries, so the Akamai brand does not imply complete integration.
Algolia packages indexing, search-as-you-type, facets, and UI components as a hosted service; it ships quickly, but data synchronization, relevance, and usage costs remain your responsibility.
Kafka is a distributed log ordered by partition and retained by policy. Consumer groups divide work through offsets, while exactly-once processing holds only inside boundaries covered by Kafka transactions.
Pulsar separates serving from storage: stateless brokers handle connections while BookKeeper stores ledgers and subscription cursors. Elasticity and multi-tenancy come with more operational components.
Appwrite combines Auth, TablesDB, Storage, Functions, Realtime, and Messaging behind consistent APIs; Cloud and self-hosted products resemble each other but have different operational ownership.
assistant-ui separates Agent Chat into headless React primitives, a conversation runtime, and backend adapters, so the UI does not have to bind directly to one model SDK's message state.
App Runner packages build, deployment, TLS, load balancing, and autoscaling as a web service, trading orchestration control for a simpler platform with explicit VPC, instance, and health boundaries.
Fargate removes container-host operations, but task definitions, ECS services, VPCs, IAM, scaling, deployment, and observability remain your system.
Lambda fits short-lived, event-driven, bursty work; its design center is invocation, retries, idempotency, and downstream capacity—not merely smaller containers.