Teams now run fleets of coding agents and production-facing MCP tools. Concord gives each task a governed harness — code, deploy, live investigation, bootstrap job, or data product — and records the proof in a single source of truthful intelligence.
✈️ Air-traffic control for code, runtime evidence, data contracts and deploy checks — across Claude · Codex · GeminiThis isn't a framework we hope you'll try — it's the governed execution layer behind our production AI. The case studies on this site shipped through Concord — the “single source of truthful intelligence” we named in 2020, now the system we run on.
Production AI projects governed end-to-end — pharma, clinical, claims, education, hospitality, logistics and data.
Governed, evidence-backed changes shipped — each attributed, gated and journaled.
Peak concurrent agents on a single project — dozens a day — coordinated with no collisions or lost writes.
softsensor.ai itself is built, gated and deployed through Concord.
Across these projects Concord has coordinated 500+ agent identities over 1,200+ sessions and 17,000+ governed events — both orchestrated sub-agents and independent terminal agents, governed identically. Figures reflect Softsensor's own governed delivery; client names are withheld for confidentiality.
Lineage: Concord grew out of AI Agent Setup — our open-source, skill-aware tooling for standing up Claude, Codex and Gemini on any repo (2025, Apache-2.0). First we made agents easy to set up; then we made them accountable.
Running parallel coding agents is now a commodity. The unsolved part is proving what happened before merge, after deploy, and inside production-facing investigations.
Parallel agents race on shared files, overwrite each other's work, and leave ownership of every change unclear.
Prompts, plans, test runs, live reads, bootstrap jobs and rationale scatter across terminals and chat logs — gone by the time anyone asks.
Review evidence, traceability, runtime proof and change-control history are mandatory however the work was generated.
The agents are the workforce; Concord is the control plane that coordinates them, gives each a role, and accounts for everything they do — across repos, providers, runtime tools, and proof types.
Every agent works in its own git worktree behind a per-ticket lock — so a fleet runs in parallel, across many repositories, with clear ownership and zero overwrites.
Governed roles take work from plan → build → review → test → land, in either topology: independent agents, or an orchestrator directing sub-agents.
Every transition is attributed to a specific agent, under a specific reviewer, in an append-only journal — so multi-agent work is replayable and auditable.
Every unit of agent work runs the same path — and lands in a tamper-evident journal. The proof changes by track, so a live production read is not verified like a code diff.
Worktree + branch + lock per ticket. Many agents, many repos — no collisions.
Tests for code, content checks for pages, receipts for live-MCP work, data contracts for analytics, deploy evidence for runtime work.
Every transition recorded with actor and provenance — a verifiable history of who did what, under whose review.
A read-only web view renders the board, traceability, gates, live-MCP receipts, bootstrap risk, runtime health and timeline.
Concord now governs more than code diffs. It gives each work type the evidence standard it actually needs, while keeping one board, one journal and one recovery model.
Per-ticket worktrees, locks, review cycles, feature proof, contract checks, quality dimensions and source-to-landed commit lineage.
Production-MCP investigations, deploy checks and bootstrap/backfill jobs record scoped, redacted, source-cited evidence instead of relying on "readyz returned 200".
Analytical products certify data contracts, row-count reconciliation, lineage and quality gates. The same journal feeds governed memory: cited decisions, summaries and recall.
Branch protection governs the pull request. Concord governs the agent work before the PR — and turns it into a record an auditor can use.
Required checks, protected branches, CODEOWNERS, approvals. Strong process enforcement — but no record of which agent did what before the PR, no runtime proof after deploy, and no governed memory of the decision trail.
Task locks, worktree isolation, requirement closure, agent provenance, track-specific proof, runtime receipts, fail-closed review and governed memory — across providers, repositories and live engineering workflows.
AI coding agents and MCP-powered runtime work don't remove SDLC, CSV or change-control expectations. Concord is built for the regimes you already answer to — producing review evidence, traceability, runtime receipts and change history as a by-product of the workflow.
Per-change closure, review cycles and an attributable audit trail — the records these regimes ask for.
Traceability and fail-closed, control-mapped export — aligned to the direction of the FDA's Computer Software Assurance guidance (records over screenshots).
Indicative control maps included; the evidence export fails closed if anything required is missing.
We describe Concord as designed for / building toward these regimes — it produces evidence and indicative maps, not a certification, validation, or claim of conformity.
Governance adds structure — but it removes waste, because Concord holds the context, decisions and evidence agents otherwise re-pay to rediscover.
Cheap pre-checks classify already-satisfied work before an agent is ever dispatched.
Decision records, summary tiers, recall and graph context let agents retrieve the relevant slice instead of replaying the whole history.
Evidence depth follows the track — lighter for mechanical edits, stricter for production reads, data products, deploys and bootstrap jobs.
A cost-ledger records spend per change in the same journal that proves the controls — so savings are measured, not estimated. Blended figures will be published from design-partner pilots.
The repo-local governed workflow is open source. Enterprise packaging adds org-wide collection, policy and deployment patterns for customers who need central command-center evidence.
Run coding agents, production-MCP investigations, bootstrap jobs and data products through one governed execution record.